Method for generating house type structure diagram

Abstract

The embodiment of the invention discloses a method for generating a house type structure chart, and the method comprises the steps: obtaining the first point cloud data information of a target house and the track information of a collection device of the first point cloud data, and enabling the first point cloud data information to be the original point cloud data information of the target house; determining a house type structure diagram of the target house according to the first point cloud data information and the track information; and outputting the house type structure diagram of the target house, thereby obtaining the structure data of the target house through the point cloud data, achieving the automatic drawing of the house type structure diagram, and improving the precision of house measurement.

Description

Technical Field

[0001] The present invention relates to three-dimensional reconstruction technology and point cloud processing technology, and in particular to a method for generating a house floor plan diagram. Background Art

[0002] In traditional house measurement, surveyors usually use a tape measure or laser rangefinder to draw a rough sketch of the house based on their understanding of the space of the entire house, then record the measured dimensions, and finally draw the entire house in CAD (Computer Aided Drafting) drawing software to form a floor plan of the house. Traditional house measurement has the following problems. For example, it requires certain professionalism from surveyors. They need to make every measurement accurate and not miss any details in order to draw the entire house in CAD. In addition, if there are errors in the measurement records, they can only return to the site to measure again. For situations that cannot be measured again, they can only make corrections based on experience. Therefore, for traditional house measurement, the measurement accuracy is difficult to guarantee. Summary of the invention

[0003] In order to solve the technical problems in the related art, the embodiment of the present disclosure provides a method for generating a house floor plan.

[0004] According to a first aspect of an embodiment of the present disclosure, a method for generating a house floor plan diagram is provided, the method comprising:

[0005] Acquire first point cloud data information of a target house and trajectory information of a device collecting the first point cloud data;

[0006] Determine a house floor plan of the target house according to the first point cloud data information and trajectory information;

[0007] Output the house floor plan of the target house.

[0008] As an optional embodiment of the present disclosure, determining the house floor plan of the target house according to the first point cloud data information and the trajectory information includes:

[0009] Performing denoising processing on the first point cloud data information to obtain second point cloud data information;

[0010] Determine the spatial structure information of the target house based on the second point cloud data information;

[0011] Determine the inner and outer contour information of the target house based on the spatial structure information of the target house, and obtain a first house floor plan of the target house;

[0012] The window structure of the target house is determined based on the first house floor plan diagram of the target house to obtain a second house floor plan diagram, wherein the second house floor plan diagram is the final house floor plan diagram of the target house.

[0013] As an optional embodiment of the present disclosure, the denoising process is performed on the first point cloud data information to obtain the second point cloud data information, including:

[0014] A preset point cloud dynamic object removal algorithm is used to remove a first noise point cloud in the first point cloud data information to obtain the second point cloud data information, wherein the first noise point cloud is dynamic object noise information.

[0015] As an optional embodiment of the present disclosure, the performing denoising processing on the first point cloud data information to obtain the second point cloud data information further includes:

[0016] Determining a coordinate range of a mirror plane from the first point cloud data information;

[0017] Determine the point cloud data information in the first point cloud data information and in the coordinate range of the mirror plane as a second noise point cloud;

[0018] The second noise point cloud is deleted from the first point cloud data information to obtain the second point cloud data information.

[0019] As an optional embodiment of the present disclosure, the performing denoising processing on the first point cloud data information to obtain the second point cloud data information further includes:

[0020] Projecting the first point cloud data information onto a first preset plane and into a preset grid to obtain a first occupancy grid map, wherein the first preset plane is a plane formed by a horizontal axis and a vertical axis in a point cloud three-dimensional coordinate system;

[0021] Projecting the trajectory information of the acquisition device of the first point cloud data onto a first preset plane to obtain two-dimensional trajectory information of the acquisition device on the first preset plane;

[0022] Determine all connected domains formed by the first point cloud data information according to the first occupancy grid map;

[0023] Determining a noise connected domain from all the connected domains according to the two-dimensional trajectory information of the acquisition device on the first preset plane;

[0024] Determine the point cloud data information in the first point cloud data information and in the noise connected domain as a third noise point cloud;

[0025] The third noise point cloud is deleted from the first point cloud data information to obtain second point cloud data information.

[0026] As an optional embodiment of the present disclosure, the performing denoising processing on the first point cloud data information to obtain the second point cloud data information further includes:

[0027] Before projecting the first point cloud data information onto the first preset plane and into the first preset grid to obtain a first occupancy grid map, the normal of the first point cloud data information is adjusted according to the first average normal distribution of the first point cloud data information to obtain a second average normal distribution of the first point cloud data information, wherein the second average normal distribution of the first point cloud data information is perpendicular to the first preset plane.

[0028] As an optional embodiment of the present disclosure, the performing denoising processing on the first point cloud data information to obtain the second point cloud data information further includes:

[0029] Determine two planes of any wall of the target house according to the first point cloud data information;

[0030] Determine the point cloud data information in the first point cloud data information and between two planes of any wall of the target house as a fourth noise point cloud;

[0031] The fourth noise point cloud is deleted from the first point cloud data information to obtain second point cloud data information.

[0032] As an optional embodiment of the present disclosure, determining the spatial structure information of the target house based on the second point cloud data information includes:

[0033] Determining a wall plane of the target house based on the second point cloud data information;

[0034] Determine a first outer rectangular contour according to an outer bounding box of the second point cloud data information;

[0035] The spatial structural information of the target house is determined using the wall plane and the first outer rectangular outline.

[0036] As an optional embodiment of the present disclosure, determining the wall plane of the target house based on the second point cloud data information includes:

[0037] Determine all spatial planes of the target house based on the second point cloud data information by using a region growing method and a regional average normal;

[0038] Determine a vertical plane from all spatial planes of the target house using a preset plane equation;

[0039] Projecting the second point cloud data information onto a second preset plane and into a second preset grid to obtain a second occupancy grid map, wherein the second preset plane is a plane formed by a horizontal axis and a vertical axis in a point cloud three-dimensional coordinate system;

[0040] Deleting the vertical plane located inside the second occupancy grid map to obtain a vertical plane located at an edge of the second occupancy grid map;

[0041] The vertical plane located at the edge of the second occupancy grid map is determined as the wall plane of the target house.

[0042] As an optional embodiment of the present disclosure, the determining the spatial structure information of the target house by using the wall plane and the first outer rectangular outline includes:

[0043] Enlarging and adjusting the outline size of the first outer rectangular outline to obtain a second outer rectangular outline;

[0044] Projecting the point cloud data information of the wall plane onto the second preset plane to obtain the wall line segments of the wall plane;

[0045] The spatial structural information of the target house is determined using the wall line segments of the wall plane and the second outer rectangular outline.

[0046] As an optional embodiment of the present disclosure, the determining the spatial structural information of the target house by using the wall line segments of the wall plane and the second outer rectangular contour includes:

[0047] According to the position coordinates of the wall line segments of the wall plane, the wall line segments are added to the corresponding coordinate positions of the second outer rectangular contour to obtain the initial spatial structure of the target house;

[0048] Supplementing each wall line segment in the initial spatial structure of the target house to obtain supplemented wall line segments;

[0049] The final spatial structure formed by the supplemented wall line segments and the second outer rectangular outline is determined as the spatial structure information of the target house.

[0050] As an optional embodiment of the present disclosure, determining the inner and outer contour information of the target house based on the spatial structure information of the target house to obtain a first house floor plan of the target house includes:

[0051] Determine a corresponding graph to be segmented based on the spatial structure information of the target house, wherein the vertices of the graph to be segmented are polygons formed by the spatial structure information of the target house, and the edges of the graph to be segmented include common edges of adjacent polygons in the polygons formed by the spatial structure information of the target house and edges formed by connecting the vertices of the graph to be segmented with the source points and the sink points of the graph cut respectively;

[0052] Determine the weight of each edge in the graph to be segmented according to the vertices of the graph to be segmented, the edges of the graph to be segmented, the graph cut source points and the sink points;

[0053] Determine the minimum cut of the graph to be segmented by using a preset graph cut algorithm based on the weight calculation of each edge in the graph to be segmented, and obtain the inner boundary and the outer boundary of the spatial structure of the target house;

[0054] The inner boundary and the outer boundary edges of the spatial structure of the target house are connected in series to obtain a first house floor plan diagram of the target house.

[0055] As an optional embodiment of the present disclosure, the inner boundary and the outer boundary edges of the spatial structure of the target house are connected in series to obtain a first house floor plan diagram of the target house, including:

[0056] Connecting the inner boundary and the outer boundary of the spatial structure of the target house in series to obtain a polygonal outline of the target house;

[0057] Determine any two vertices of the to-be-segmented graph in the polygonal outline that meet a first preset merging condition as first target merging vertices, or determine a second target merging vertex from a plurality of vertices in the polygonal outline that meet a second preset merging condition;

[0058] The first target merged vertex or the second target merged vertex is merged to obtain a merged polygonal outline, where the merged polygonal outline is a first house floor plan of the target house.

[0059] As an optional embodiment of the present disclosure, the window structure of the target house is determined based on the first house floor plan diagram of the target house to obtain a second house floor plan diagram, wherein the second house floor plan diagram is a final house floor plan diagram of the target house, including:

[0060] Obtaining point cloud data information corresponding to the edges of each contour in the first house layout diagram;

[0061] Projecting the point cloud data information corresponding to the edge of each contour onto a third preset plane and a third preset grid to obtain a third occupancy grid map, wherein the third preset plane is a plane formed by the longitudinal axis and the vertical axis in the point cloud three-dimensional coordinate system;

[0062] For any edge of a contour, determining in the corresponding third occupancy grid map whether it includes a hole area;

[0063] If it is determined that the hole area is included, a rectangular bounding box of the hole area is determined to obtain the size and coordinate position of the hole area, and the hole area is a window structure on the edge of the corresponding outline in the first house floor plan;

[0064] The hollow area is drawn on the edge of the corresponding outline in the first house floor plan diagram according to the size and coordinate position of the hollow area to obtain the second house floor plan diagram.

[0065] According to a second aspect of an embodiment of the present disclosure, a device for generating a house floor plan diagram is provided, the device comprising:

[0066] A data acquisition module, used to acquire first point cloud data information of a target house and trajectory information of a collection device of the first point cloud data;

[0067] A housing floor plan structure diagram determining module, configured to determine a housing floor plan structure diagram of the target house according to the first point cloud data information and trajectory information;

[0068] The house floor plan output module is used to output the house floor plan of the target house.

[0069] As an optional embodiment of the present disclosure, the housing floor plan structure determination module includes:

[0070] A denoising processing unit, configured to perform denoising on the first point cloud data information to obtain second point cloud data information;

[0071] A first determining unit, configured to determine the spatial structural information of the target house based on the second point cloud data information;

[0072] A second determining unit is used to determine the inner and outer contour information of the target house based on the spatial structure information of the target house, so as to obtain a first house floor plan of the target house;

[0073] The third determining unit is used to determine the window structure of the target house based on the first house floor plan diagram of the target house, and obtain a second house floor plan diagram, wherein the second house floor plan diagram is the final house floor plan diagram of the target house.

[0074] As an optional embodiment of the present disclosure, the denoising processing unit includes:

[0075] The first noise removal subunit is used to remove the first noise point cloud in the first point cloud data information by using a preset point cloud dynamic object removal algorithm to obtain the second point cloud data information, wherein the first noise point cloud is dynamic object noise information.

[0076] As an optional embodiment of the present disclosure, the denoising processing unit further includes:

[0077] A mirror plane determination subunit, used to determine the coordinate range of the mirror plane from the first point cloud data information;

[0078] A first noise point cloud determining unit, configured to determine the point cloud data information in the first point cloud data information and in the coordinate range of the mirror plane as a second noise point cloud;

[0079] The second noise removal subunit deletes the second noise point cloud from the first point cloud data information to obtain the second point cloud data information.

[0080] As an optional embodiment of the present disclosure, the denoising processing unit further includes:

[0081] A first projection subunit projects the first point cloud data information onto a first preset plane into a preset grid to obtain a first occupancy grid map, wherein the first preset plane is a plane formed by a horizontal axis and a vertical axis in a point cloud three-dimensional coordinate system;

[0082] A second projection subunit is used to project the trajectory information of the acquisition device of the first point cloud data onto a first preset plane to obtain two-dimensional trajectory information of the acquisition device on the first preset plane;

[0083] a connected domain determining subunit, configured to determine all connected domains formed by the first point cloud data information according to the first occupancy grid map;

[0084] A noise connected domain determining subunit, configured to determine a noise connected domain from all connected domains according to the two-dimensional trajectory information of the acquisition device on the first preset plane;

[0085] A second noise point cloud determining unit, configured to determine the point cloud data information in the first point cloud data information and in the noise connected domain as a third noise point cloud;

[0086] The third noise removal subunit is used to delete the third noise point cloud from the first point cloud data information to obtain second point cloud data information.

[0087] As an optional embodiment of the present disclosure, the denoising processing unit further includes:

[0088] The second projection subunit is used to adjust the normal of the first point cloud data information according to the first average normal distribution of the first point cloud data information before projecting the first point cloud data information onto the first preset plane into the first preset grid to obtain a first occupancy grid map, so as to obtain a second average normal distribution of the first point cloud data information, wherein the second average normal distribution of the first point cloud data information is perpendicular to the first preset plane.

[0089] As an optional embodiment of the present disclosure, the denoising processing unit further includes:

[0090] A first wall surface determination subunit, configured to determine two planes of any wall of the target house according to the first point cloud data information;

[0091] A third noise point cloud determining unit, configured to determine point cloud data information in the first point cloud data information and between two planes of any wall of the target house as a fourth noise point cloud;

[0092] The fourth noise removal subunit is used to delete the fourth noise point cloud from the first point cloud data information to obtain second point cloud data information.

[0093] As an optional embodiment of the present disclosure, the first determining unit includes:

[0094] A second wall surface determination subunit, configured to determine a wall plane of the target house based on the second point cloud data information;

[0095] A rectangular outline determining subunit, configured to determine a first outer rectangular outline according to an outer bounding box of the second point cloud data information;

[0096] The spatial structure determination subunit is used to determine the spatial structure information of the target house by using the wall plane and the first outer rectangular outline.

[0097] As an optional embodiment of the present disclosure, the second wall surface determining subunit is further configured to:

[0098] Determine all spatial planes of the target house based on the second point cloud data information by using a region growing method and a regional average normal;

[0099] Determine a vertical plane from all spatial planes of the target house using a preset plane equation;

[0100] Projecting the second point cloud data information onto a second preset plane and into a second preset grid to obtain a second occupancy grid map, wherein the second preset plane is a plane formed by a horizontal axis and a vertical axis in a point cloud three-dimensional coordinate system;

[0101] Deleting the vertical plane located inside the second occupancy grid map to obtain a vertical plane located at an edge of the second occupancy grid map;

[0102] The vertical plane located at the edge of the second occupancy grid map is determined as the wall plane of the target house.

[0103] As an optional embodiment of the present disclosure, the spatial structure determination subunit is further used to:

[0104] Enlarging and adjusting the outline size of the first outer rectangular outline to obtain a second outer rectangular outline;

[0105] Projecting the point cloud data information of the wall plane onto the second preset plane to obtain the wall line segments of the wall plane;

[0106] The spatial structural information of the target house is determined using the wall line segments of the wall plane and the second outer rectangular outline.

[0107] As an optional embodiment of the present disclosure, the spatial structure determination subunit determines the spatial structure information of the target house using the wall line segments of the wall plane and the second outer rectangular contour, including:

[0108] According to the position coordinates of the wall line segments of the wall plane, the wall line segments are added to the corresponding coordinate positions of the second outer rectangular contour to obtain the initial spatial structure of the target house;

[0109] Supplementing each wall line segment in the initial spatial structure of the target house to obtain supplemented wall line segments;

[0110] The final spatial structure formed by the supplemented wall line segments and the second outer rectangular outline is determined as the spatial structure information of the target house.

[0111] As an optional embodiment of the present disclosure, the second determining unit includes:

[0112] A graph-to-be-segmented determining subunit is used to determine a corresponding graph to be segmented based on the spatial structural information of the target house, wherein the vertices of the graph to be segmented are polygons formed by the spatial structural information of the target house, and the edges of the graph to be segmented include common edges of adjacent polygons in the polygons formed by the spatial structural information of the target house and edges formed by connecting the vertices of the graph to be segmented with the source points and sink points of the graph cut respectively;

[0113] A weight determination subunit, used to determine the weight of each edge in the graph to be segmented according to the vertices of the graph to be segmented, the edges of the graph to be segmented, the graph cut source points and the sink points;

[0114] A minimum cut determination subunit is used to determine the minimum cut of the graph to be segmented based on the weight calculation of each edge in the graph to be segmented using a preset graph cut algorithm, so as to obtain the inner boundary and the outer boundary of the spatial structure of the target house;

[0115] The connection subunit is used to connect the inner boundary and the outer boundary of the spatial structure of the target house in series to obtain a first house floor plan diagram of the target house.

[0116] As an optional embodiment of the present disclosure, the connection subunit connects the inner boundary and the outer boundary of the spatial structure of the target house in series to obtain a first house floor plan diagram of the target house, including:

[0117] Connecting the inner boundary and the outer boundary of the spatial structure of the target house in series to obtain a polygonal outline of the target house;

[0118] Determine any two vertices of the to-be-segmented graph in the polygonal outline that meet a first preset merging condition as first target merging vertices, or determine a second target merging vertex from a plurality of vertices in the polygonal outline that meet a second preset merging condition;

[0119] The first target merged vertex or the second target merged vertex is merged to obtain a merged polygonal outline, where the merged polygonal outline is a first house floor plan of the target house.

[0120] As an optional embodiment of the present disclosure, the third determining unit includes:

[0121] A contour point cloud acquisition subunit, configured to acquire point cloud data information corresponding to edges of each contour in the first house floor plan diagram based on the first house floor plan diagram of the target house;

[0122] A third projection subunit is used to project the point cloud data information corresponding to the edge of each contour onto a third preset plane and into a third preset grid to obtain a third occupancy grid map, wherein the third preset plane is a plane formed by the longitudinal axis and the vertical axis in the point cloud three-dimensional coordinate system;

[0123] A hole region determination subunit, used for determining, for an edge of any contour, whether a hole region is included in the third occupancy grid map corresponding to the edge of any contour;

[0124] a window structure determination subunit, for determining a rectangular bounding box of the hole area if it is determined that the hole area is included, and obtaining the size and coordinate position of the hole area, wherein the hole area is a window structure on the edge of the corresponding outline in the first house floor plan;

[0125] The form drawing subunit is used to draw the hollow area on the edge of the corresponding outline in the first house floor plan according to the size and coordinate position of the hollow area to obtain the second house floor plan.

[0126] According to a third aspect of an embodiment of the present disclosure, there is provided an electronic device, including:

[0127] A memory for storing a computer program product;

[0128] The processor is used to execute the computer program product stored in the memory, and when the computer program product is executed, the method described in the first aspect above is implemented.

[0129] According to a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method described in the first aspect above is implemented.

[0130] According to a fifth aspect of an embodiment of the present disclosure, a computer program product is provided, including computer program instructions, which, when executed by a processor, implement the method described in the first aspect above.

[0131] Through the technical solution of the embodiment of the present disclosure, the point cloud data of the target house collected by the point cloud data collection device and the trajectory information of the collection device are obtained, and then the house floor plan structure diagram of the target house is automatically generated based on the point cloud data information and the trajectory information, and then the house floor plan structure diagram of the target house is output. In the present disclosure, the structural data of the target house is obtained through point cloud data to realize the automatic drawing of the house floor plan structure diagram. Compared with the traditional house measurement, the point cloud data can more objectively reflect the structural parameters of the house, without relying on the professionalism and experience of the surveyor, thereby improving the accuracy of the house measurement and realizing a more accurate house floor plan structure diagram.

[0132] The technical solution of the present disclosure is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0133] The accompanying drawings, which constitute a part of the specification, illustrate embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0134] The present disclosure may be more clearly understood from the following detailed description with reference to the accompanying drawings, in which:

[0135] Figure 1 The present invention is a flowchart of a method for generating a house floor plan diagram according to an embodiment of the method disclosed herein.

[0136] Figure 2The present invention is a flowchart of a method for generating a house floor plan diagram according to another embodiment of the method disclosed herein.

[0137] Figure 3 The present invention is a flowchart of a method for generating a house floor plan diagram according to another embodiment of the method disclosed herein.

[0138] Figure 4 This is a schematic diagram of the effect of the spatial structure of a target house according to an embodiment of the method disclosed herein.

[0139] Figure 5 The present invention is a flowchart of a method for generating a house floor plan diagram according to another embodiment of the method disclosed herein.

[0140] Figure 6 This is one of the schematic diagrams of using a graph cut algorithm to determine the internal and external contour information of a target house in an embodiment of the method disclosed herein.

[0141] Figure 7 This is a second schematic diagram of determining the internal and external contour information of a target house using a graph cut algorithm according to an embodiment of the method disclosed herein.

[0142] Figure 8 This is a third schematic diagram of using a graph cut algorithm to determine the internal and external contour information of a target house according to an embodiment of the method disclosed herein.

[0143] Fig. 9 The present invention is a flowchart of a method for generating a house floor plan diagram according to another embodiment of the method disclosed herein.

[0144] Fig.10 The figure is a schematic diagram of determining a window structure according to an embodiment of the method disclosed herein.

[0145] Fig.11 This is a schematic diagram of a final house floor plan diagram of an embodiment of the method disclosed herein.

[0146] Fig.12 The present invention is a structural block diagram of a device for generating a house floor plan diagram according to an embodiment of the present invention.

[0147] Fig.13 This is a structural block diagram of a device for generating a house floor plan diagram according to another embodiment of the device disclosed herein.

[0148] Fig.14 The present invention is a block diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0149] In the disclosed embodiment, an image acquisition device such as a laser radar can be used to perform an all-round scan of the house space to obtain point cloud data of surfaces such as walls, ceilings, and floors, thereby obtaining a point cloud data image of the house space. It can be understood that the point cloud data image of the house space is composed of a large number of discrete points, and there is no topological relationship between these discrete points, but each point contains spatial coordinates and possible other attribute information. Therefore, the disclosed embodiment uses the point cloud data of the house space to obtain the structural data of the target house, so as to realize the automatic drawing of the house floor plan structure diagram.

[0150] In order for those skilled in the art to clearly understand the technical solution of the embodiments of the present disclosure, the technical solution of the embodiments of the present disclosure is described in detail below with reference to the accompanying drawings.

[0151] Figure 1 FIG. 1 is a flow chart of a method for generating a house floor plan diagram according to an embodiment of the method disclosed herein. Figure 1 As shown, a method for generating a house floor plan diagram according to an embodiment of the present disclosure may include the following steps:

[0152] Step 101: Acquire first point cloud data information of a target house and trajectory information of a device collecting the first point cloud data.

[0153] In the disclosed embodiment, the planes of various spaces of the target house can be scanned in all directions by image acquisition equipment such as laser radar, for example, the ceiling, floor, wall, etc. of the target house, to obtain point cloud data information of the target house. It is understandable that the target house may include door panels, wardrobes, cabinets, refrigerators, indoor mirrors and other auxiliary structures in addition to its own structure (ceiling, floor, wall), so the point cloud data information will also include point cloud data of these auxiliary structures.

[0154] In the disclosed embodiment, in order to comprehensively collect point cloud data information of the target house, the collection device can be moved to traverse every space of the target house and all planes of each space to obtain more complete point cloud data information of the target house.

[0155] Step 102, determining a house floor plan of the target house according to the first point cloud data information and the trajectory information.

[0156] The point cloud data information of the target house is composed of a large number of discrete points, each of which contains spatial coordinates and possible other attribute information. Therefore, the disclosed embodiment uses the spatial coordinates of the point cloud data of each space of the target house to obtain the spatial structure data of each space, and the trajectory information of the acquisition device can assist in the division of the spatial structure data of each space of the target house, and finally realize the automatic drawing of the house floor plan structure diagram.

[0157] The house floor plan diagram may be, for example, a CAD drawing, which includes the structural division of each space of the target house, auxiliary structures of the target house such as windows, doors, etc., and may also include information such as the scale size of each space and auxiliary structure.

[0158] Step 103, outputting the house floor plan of the target house.

[0159] After the house floor plan of the target house is completed, the house floor plan can be output. For example, an output instruction can be received from an input device, and the house floor plan can be output in a specific file format or displayed in, for example, CAD drawing software.

[0160] Through the technical solution of the embodiment of the present disclosure, the point cloud data of the target house collected by the point cloud data collection device and the trajectory information of the collection device are obtained, and then the house floor plan structure diagram of the target house is automatically generated based on the point cloud data information and the trajectory information, and the house floor plan structure diagram of the target house is output. In the present disclosure, the structural data of the target house is obtained through point cloud data to realize the automatic drawing of the house floor plan structure diagram. Compared with the traditional house measurement, the point cloud data can more objectively reflect the structural parameters of the house, without relying on the professional degree and experience of the surveyor, improve the accuracy of the house measurement, and realize a more accurate house floor plan structure diagram.

[0161] exist Figure 1 Based on the illustrated embodiment, the present disclosure also provides another embodiment. Figure 2 The present invention is a flowchart of a method for generating a house floor plan diagram according to another embodiment of the method disclosed herein.

[0162] like Figure 2 As shown, a method for generating a house floor plan diagram according to an embodiment of the present disclosure may further include the following steps:

[0163] Step 101: Acquire first point cloud data information of a target house and trajectory information of a device collecting the first point cloud data.

[0164] See for example Figure 1 For the sake of brevity, step 101 is not described again here.

[0165] Step 1021: denoising the first point cloud data information to obtain second point cloud data information.

[0166] During the point cloud data information collection process, the indoor living environment of the house is relatively complex. For example, indoor cabinets, refrigerators and other furniture and appliances will block the walls, reflective objects such as mirrors will interfere with the collection of point cloud data by the collection equipment, objects outdoors through windows may also be collected, and there may even be people walking in the room, etc. These will cause the original point cloud data information (first point cloud data information) collected by the collection equipment to be very messy.

[0167] Based on this, the embodiments of the present disclosure provide a solution for denoising point cloud data information to obtain cleaner point cloud data information, reduce the interference of noisy point cloud data, and improve the accuracy of house floor plan diagrams.

[0168] As an embodiment of the present disclosure, a preset point cloud dynamic object removal algorithm can be used to remove the first noise point cloud in the first point cloud data information to obtain the second point cloud data information, wherein the first noise point cloud is the noise information of the dynamic object. Exemplarily, the preset point cloud dynamic object removal algorithm uses a multi-resolution range image (range image or depth image) to identify the dynamic object, and uses the visibility of the projected range image to identify the moving point, thereby dividing the first point cloud data information into a dynamic point cloud and a static point cloud, wherein the dynamic point cloud is the point cloud data information of the dynamic object (such as a person walking in the room), which is determined as the first noise point cloud, and the first noise point cloud is deleted to obtain the second point cloud data information. It can be understood that the preset point cloud dynamic object removal algorithm includes but is not limited to the method of identifying dynamic objects with a multi-resolution range image, which will not be repeated here. In the embodiment of the present disclosure, the preset point cloud dynamic object removal algorithm is used to identify the dynamic object, thereby removing the point cloud of the dynamic object to obtain a cleaner and more accurate point cloud data information of the indoor space of the target house, reduce the influence of the noise point cloud data, and improve the accuracy of the house floor plan.

[0169] As another embodiment of the present disclosure, denoising can also be performed on the point cloud data information emitted by the mirror plane. For example, the wall in the mirror is the point cloud data information reflected by the mirror. Such point cloud data information is a noise point cloud and needs to be denoised. Specifically, the coordinate range of the mirror plane can be determined from the first point cloud data information, and then the point cloud data information of the coordinate range of the mirror plane in the first point cloud data information is determined as the second noise point cloud, and the second noise point cloud is deleted from the first point cloud data information to obtain the second point cloud data information. Exemplarily, the high-intensity echo characteristics when the laser is vertically incident on the mirror can be used to determine all the mirror planes in the target house, and then the mirror frame point cloud (if it is a frameless mirror attached to the wall or cabinet, the plane point cloud of the mirror boundary is used) to restore the range of the mirror, and all the point cloud data information collected by the light passing through the inside of the mirror range are deleted.

[0170] As another embodiment of the present disclosure, since the target house may have a glass window and an auxiliary structure, some objects outside the target house may be seen through the glass window, and these objects may also be collected by the acquisition device, but these objects do not belong to the target house. Based on this, it is necessary to perform denoising on the point cloud data information of these objects. Specifically, the first point cloud data information is projected to the preset grid on the first preset plane to obtain a first occupancy grid map, wherein the first preset plane is a plane formed by the horizontal axis and the vertical axis in the three-dimensional coordinate system of the point cloud, and the trajectory information of the acquisition device of the first point cloud data is projected to the first preset plane to obtain the two-dimensional trajectory information of the acquisition device in the first preset plane, all connected domains formed by the first point cloud data information are determined according to the first occupancy grid map, and the noise connected domain is determined from all connected domains according to the two-dimensional trajectory information of the acquisition device in the first preset plane, and then the point cloud information in the first point cloud data and in the noise connected domain is determined as a third noise point cloud, and the third noise point cloud is deleted from the first point cloud data information to obtain the second point cloud data information. In this embodiment, the point cloud data information of the target house is projected onto a grid map, and point clouds with similar coordinate positions can be grouped together (for example, in one grid) to obtain the main indoor part of the target house. In addition, since the acquisition device collects indoors in the target house, the area through which the two-dimensional trajectory information formed is indoors, thereby more accurately determining the non-indoor part, that is, the non-indoor connected domain, and deleting the point cloud data information in the non-indoor connected domain.

[0171] In the point cloud coordinate system, the ground is not necessarily horizontal. By statistically analyzing the normal distribution of the point cloud, the point cloud of the target house can be adjusted in angle, for example, rotated to be parallel to the ground. Specifically, before projecting the first point cloud data information to the first preset plane into the first preset grid to obtain the first occupancy grid map, the normal of the first point cloud data information is adjusted according to the first average normal distribution of the first point cloud data information to obtain the second average normal distribution of the first point cloud data information, wherein the second normal distribution of the first point cloud data information is perpendicular to the first preset plane. Then, the point cloud of the target house is projected onto the horizontal plane to form an occupancy grid map, and the trajectory position is also projected onto the horizontal plane to form two-dimensional trajectory information, and then each connected domain is extracted on the occupancy grid map (i.e., the first occupancy grid map), and the connected domain without two-dimensional trajectory information is filtered out. In this way, the point cloud data on the outside of the target house collected through the glass window is removed to obtain more accurate point cloud data information of the target house.

[0172] The average normal distribution of the first point cloud data information refers to the average value of the normal vector of each point in the point cloud, which is used to describe the overall directional distribution characteristics of the point cloud. The statistical calculation of the average normal distribution can be achieved, for example, by least squares plane fitting. For the average normal distribution of cloud data information on a plane, the normal line method can be used to express it as ax+by+cz=d, where a 2 +b 2 +c 2 =1, construct the cost function by the least square method to make the plane most accurate, and then estimate the average normal distribution of the plane surface by solving the function.

[0173] As another embodiment of the present disclosure, there may also be noise point clouds caused by various reasons inside the wall of the target house. In the embodiment of the present disclosure, the internal and external structures of a certain wall structure can be determined first, and then the internal structure of the wall can be determined, and then the point cloud appearing in the internal structure can be denoised. Specifically, two planes of any wall of the target house can be determined based on the first point cloud data information, and the point cloud data information between the two planes of any wall of the target house in the first point cloud data information is determined as a fourth noise point cloud, and the fourth noise point cloud is deleted from the first point cloud data information to obtain the second point cloud data information. Exemplarily, the point cloud data information on both sides of the wall can be used. For example, if the normals of two planes are opposite and the positions reflected by the point cloud data are close, the two planes are determined as two planes of a certain wall. If point clouds appear inside the two planes, this part of the point cloud data is determined as a noise point cloud and denoised.

[0174] Step 1022: determine the spatial structure information of the target house based on the second point cloud data information.

[0175] The spatial structure information includes at least the maximum spatial range that can be mapped by the point cloud data information of the target house and the spatial structure composition of the target house (such as the number of walls, the location of walls, and the various spaces composed of walls).

[0176] As an embodiment of the present disclosure, determining the spatial structure information of the target house may be specifically achieved through the following steps. Figure 3 FIG. 1 is a flow chart of a method for generating a house floor plan diagram according to another embodiment of the method disclosed herein. Figure 3 As shown, determining the spatial structure information of the target house may include the following steps:

[0177] Step 1022a: determine the wall plane of the target house based on the second point cloud data information.

[0178] Specifically, firstly, the region growing method and the region averaging method can be used to determine all the spatial planes of the target house based on the first point cloud data information.

[0179] In the embodiment of the present disclosure, the average normal of the region (i.e., the average normal of the plane region of a certain wall) is combined with the region growing method to determine the spatial plane (for example, including but not limited to the plane of the wall, the floor plane, the ceiling plane, etc.). Specifically, when the region growing method is used for region growth, in addition to judging whether the angle between the point cloud normal and the neighborhood point normal meets the preset angle threshold, it is also necessary to judge whether the angle between the average normal of the plane region of a certain wall as a whole meets the preset angle threshold (for example, the preset angle range is between 5° and 10°, including 5° and 10°). In this way, the region growing method is avoided from only considering local planarity. For example, when there is noise in the point cloud, the angle threshold between the point cloud normal and the neighborhood point normal is difficult to adjust. If the angle threshold is strict, some large planes will be grown into multiple small and fragmented planes. If the angle threshold is relaxed, different planes will be easily grown together in arc-shaped areas. Therefore, the embodiment of the present disclosure can more robustly extract all spatial planes of the target house and improve data accuracy.

[0180] Then, the vertical plane is determined from all the spatial planes of the target house using the preset plane equations.

[0181] It is understandable that all the spatial planes of the target house are not necessarily vertical, such as the floor, ceiling and other planes, so the wall planes need to be determined from these spatial planes. Specifically, after all the spatial planes are extracted, by restricting their plane equations (for example, ax+by+cz=0, and adding constraints to the equation (for example, the normal vector of the vertical plane is parallel to the z-axis, that is, the x and y components of the normal vector are 0, and the z component is not 0), to ensure that it is a vertical plane), it can be determined which planes are vertical planes, that is, spatial planes that meet the constraints of the plane equation.

[0182] Secondly, the second point cloud data information is projected onto a second preset plane and into a second preset grid to obtain a second occupancy grid map, wherein the second preset plane is a plane formed by a horizontal axis and a vertical axis in the point cloud three-dimensional coordinate system.

[0183] Thirdly, the vertical plane located inside the second occupancy grid map is deleted to obtain a vertical plane located at the edge of the second occupancy grid map.

[0184] Finally, the vertical plane located at the edge of the second occupancy grid map is determined as the wall plane of the target house.

[0185] The vertical plane includes not only wall planes, but also planes of large appliances such as refrigerators and door panels. Therefore, it is necessary to distinguish which are wall planes and which are non-wall planes. In the disclosed embodiment, it is assumed that the wall planes are closer to the edge of the grid and the non-wall planes are closer to the inside of the grid in the point cloud occupancy grid map compared to the non-wall planes. Based on this, the top-down projection occupancy grid map of the point cloud (i.e., the top-down projection to the second preset grid to obtain the second occupancy grid map) is used to further determine which vertical planes are located inside the second preset grid and which are located at the edge of the second preset grid, so as to eliminate the vertical planes completely located inside the space, and the remaining vertical planes located at the edge of the grid are wall planes.

[0186] Step 1022b: determining a first outer rectangular outline according to the outer bounding box of the second point cloud data information.

[0187] The outer bounding box of the second point cloud data information, that is, the outer bounding box of the point cloud, is a geometric shape used to approximate the boundary range of point cloud data, and is mainly used for fast analysis and processing of point cloud data. Common point cloud outer bounding box types include Axis-Aligned Bounding Box (AABB) and Oriented Bounding Box (OBB). AABB (Axis-Aligned Bounding Box) is an axis-aligned bounding box whose boundaries are parallel to the coordinate axes. The calculation of AABB is relatively simple, but it may not fit the shape of the point cloud closely. OBB (Oriented Bounding Box) can be rotated according to the direction of the point cloud to fit the shape of the point cloud more closely. The calculation of OBB is relatively complex, but it can provide more accurate boundary information. The disclosed embodiment uses the outer bounding box of the point cloud data information to quickly determine the boundary range of the point cloud data and improve processing efficiency.

[0188] The first outer rectangular outline is the rectangular outline formed by the boundary range corresponding to the point cloud data information of the target house.

[0189] Step 1022c, determining the spatial structural information of the target house using the wall plane and the first outer rectangular outline.

[0190] Specifically, first, the outline size of the first outer rectangular outline may be enlarged and adjusted to obtain the second outer rectangular outline.

[0191] For example, based on the first outer rectangular contour, the first outer rectangular contour can be enlarged and adjusted according to a preset size to obtain a second outer rectangular contour. The preset size can be enlarged by 5 to 10 mm according to the scale, or enlarged to 1 m according to the actual size, etc., which is not limited in the present disclosure.

[0192] Then, the point cloud data information of the wall plane is projected in the top-down direction to obtain the wall line segments of the wall plane.

[0193] Exemplarily, a straight line can be obtained by projecting any extracted wall plane onto a second preset plane (or a top view), and then the point cloud in the first point cloud data information of the wall plane is projected onto the second preset plane (or a top view) to obtain the straight line, and the three-dimensional coordinates of the point cloud are converted into two-dimensional coordinates, and then the two endpoints of the wall plane projection can be obtained on the straight line, thereby determining the range of the wall plane on the top view projection, forming a range that can be calculated on the straight line, obtaining two endpoints, and finally forming a wall line segment corresponding to the wall plane.

[0194] Finally, the spatial structural information of the target house is determined using the wall line segments of the wall plane and the second outer rectangular outline.

[0195] Furthermore, based on the position coordinates of the wall line segments in the wall plane, the wall line segments can be added to the corresponding coordinate positions of the second outer rectangular outline to obtain the initial spatial structure of the target house, and then each wall line segment in the initial spatial structure of the target house is supplemented to obtain supplemented wall line segments, and the final spatial structure formed by the supplemented wall line segments and the second outer rectangular outline is determined as the spatial structure information of the target house.

[0196] Specifically, the embodiment of the present disclosure is to add wall line segments in sequence to the rectangular area formed by the second outer rectangular outline. It can be understood that the wall line segments retain their two-dimensional coordinates in the two-dimensional coordinate system, so their two-dimensional coordinates can be used to determine their corresponding positions. All wall line segments are added in sequence to the corresponding positions of the rectangular area formed by the second outer rectangular outline. If an endpoint of any wall line segment fails to intersect with other existing wall line segments, the wall line segment can be extended until it intersects with other existing wall line segments in the space. In this way, each time a wall line segment is added, the space of the rectangular area formed by the second outer rectangular outline will be divided into more fragmented spaces, thereby forming the spatial structure of the target house and obtaining the corresponding spatial structure information.

[0197] As an optional embodiment, in addition to the wall planes, additional line segments can be added to the rectangular area formed by the second outer rectangular outline. Due to factors such as object obstruction in the target house, it is impossible to extract all wall planes, resulting in insufficient division of the spatial structure of the target house. Based on this, in the embodiment of the present disclosure, two line segments perpendicular to the wall line segment can be added to both ends of the wall line segment corresponding to each wall plane. Exemplarily, the contour lines of the top-down occupancy grid map can be used to add two line segments perpendicular to the wall line segment. This method of supplementing and adding can use the boundaries of the ceiling or the ground to assist in the supplementary addition of the wall, thereby obtaining a more complete wall line segment of the target house, providing a more accurate data basis for the house floor plan.

[0198] In summary, by dividing the wall line segments and adding the wall line segments, the rectangular area formed by the second outer rectangular outline is finally divided into a two-dimensional polygonal grid, which is the spatial structure information of the target house. Figure 4 The figure shows a schematic diagram of the spatial structure of a target house in an embodiment of the disclosed method, wherein the gray lines in the figure are wall line segments that are sequentially added to the rectangular area after extraction, and the black lines are wall line segments that are supplemented based on the existing wall line segments. For example, the line segment pointed to by "1" is a wall line segment, and the line segment pointed to by "A" that is perpendicular to the wall line segment pointed to by "1" is the supplemented wall line segment. The rectangular area formed by the entire second outer rectangular contour is divided into multiple spaces by extracting the wall line segments that are sequentially added to the rectangular area and supplementing the wall line segments to the existing wall line segments, thereby forming the spatial structure information of the target house.

[0199] Step 1023, based on the spatial structure information of the target house, determine the inner and outer contour information of the target house to obtain a first house floor plan of the target house.

[0200] For example, Figure 5 As shown, step 1023 may include the following steps:

[0201] Step 1023a, determining the corresponding graph to be segmented based on the spatial structure information of the target house.

[0202] Among them, graph cut is to divide a directed graph into two non-intersecting sets. In the embodiment of the present disclosure, the graph to be divided is the directed graph in the graph cut algorithm, whose vertices are the polygons formed by the spatial structure information of the target house, and whose edges include the common edges of adjacent polygons in the polygons formed by the spatial structure information of the target house and the vertices of the graph to be divided and the graph cut source points (for example, Figure 6 S in) and sinks (e.g. Figure 6The edges formed by the lines connecting the T in the figure. These vertices and edges form the directed graph to be segmented. Figure 6 For example, Figure 6 The rectangular polygonal areas that point A passes through to connect point i include the rectangular polygonal areas numbered 1, 2, 8, 9, 10, and 11. The common edges between these rectangular polygonal areas are the edges of the corresponding graph, such as Figure 6 The common edges of the rectangular polygonal areas numbered 11 and 12, and the common edges of the rectangular polygonal areas numbered 5 and 6, occupied by the midpoint cloud.

[0203] Step 1023b, determining the weight of each edge in the graph to be segmented according to the vertices of the graph to be segmented, the edges of the graph to be segmented, the graph cut source points and the sink points.

[0204] It is understandable that the source points of the graph cut (e.g. Figure 6 S in) and sinks (e.g. Figure 6 The minimum cut between the two edges (T in ) is used to obtain the internal and external segmentation of the space (i.e., the internal and external boundaries of the spatial structure of the target house), and each edge needs to be assigned a suitable weight. Figure 6 The circle in the polygon numbered "1" in the figure is strongly connected to the graph cut source point S, and the polygon in the outermost circle of the rectangular space ( Figure 6 The polygon numbered "12" in the figure is strongly connected to T, that is, the edge between S has infinite weight inf, the edge weight between S and T is 0, and the weights of the remaining edges are initialized to 0, and the point cloud ( Figure 6 Each point in the circle numbered a, b, ... j is connected to its emitting point (such as Figure 6 The rectangular point A in the point cloud is connected to form a line of sight, and the edge weight between the polygons through which the line of sight passes increases by w. The edge weight connected to the polygon behind each point in the point cloud and T increases. Specifically, Figure 6 For example, from rectangular point A to any point cloud (such as Figure 6 The points a, b, c, d, e, f, g, h, i, and j in the figure are connected into a line segment. The weight of the edge of the corresponding graph of the common edge passed by this line segment will increase by a fixed weight (for example, w), which is the final weight value of the second target edge, such as Figure 6 The rectangular polygonal area that point A passes through to connect point i includes the rectangular polygonal areas numbered 1, 2, 8, 9, 10, and 11. The common edges between these rectangular polygonal areas are the edges of the corresponding graph, and their corresponding weight values ​​will increase the fixed weight (such as w); while the point cloud (such as Figure 6The edges of the graphs to which points a, b, c, d, e, f, g, h, i, j belong will be given a reverse fixed weight (e.g. w') from outside to inside. The reverse fixed weight (e.g. w') represents the part with the smallest weight between the starting point S and the end point T in the graph cut algorithm, that is, the position that is easy to disconnect.

[0205] In other embodiments, in order to make the boundaries of the inner and outer contours of the target house as simple as possible, a smoothing term can be added, that is, the length of the edge between polygons is used as a weight and added to the weight of the graph to encourage the graph cut to take the shortest path possible when solving the contour.

[0206] Step 1023c, using a preset graph cut algorithm to calculate and determine the minimum cut of the graph to be segmented based on the weight of each edge in the graph to be segmented, and obtain the inner boundary and the outer boundary of the spatial structure of the target house.

[0207] After the directed graph (graph to be segmented) is determined, the graph cut algorithm is further used to calculate the minimum cut to obtain the internal boundary and external boundary of the spatial structure of the target house. Exemplarily, the disclosed embodiment can use point cloud visibility information, that is, when shooting, if a collection device such as a laser radar can collect any point, it can be determined that the line between the location of the collection device and the point (point cloud) is definitely unobstructed, and each collected point cloud will have a location of a collection device. Using this information, in the top view, the polygonal area passed by the line between the location of the collection device and the point is determined as the internal boundary of the spatial structure of the target house, and the polygonal area not passed is determined as the external boundary of the spatial structure of the target house.

[0208] Step 1023d, connecting the inner boundary and the outer boundary edges of the spatial structure of the target house in series to obtain a first house floor plan diagram of the target house.

[0209] By setting weights in the solution space, the spatial structure of the target house is eventually divided into an internal boundary and an external boundary. Then the edges of the rectangular polygon area between the internal boundary and the external boundary are connected in sequence to form a closed polygonal outline, which is the first house floor plan diagram of the target house.

[0210] See Figure 7 As shown, this is the second schematic diagram of using the graph cut algorithm to determine the internal and external contour information of the target house, that is, the schematic diagram of the result after the graph cut solution. Figure 7 In the figure, the irregular path formed by the circle is the trajectory of the acquisition device, that is, the launch point position of the point cloud. The white line represents the interior, and the gray line represents the exterior. The intersection of the two areas forms the polygonal outline of the target house, which is the first house floor plan of the target house.

[0211] However, the polygonal contour obtained directly is relatively fragmented and irregular. Based on this, the embodiment of the present disclosure provides a technical solution for further optimizing the polygonal contour to obtain a simple and regular polygonal contour.

[0212] Exemplarily, as an implementable solution of the embodiment of the present disclosure, step 1023d may also be implemented as follows: first, the edges of the inner boundary and the outer boundary of the spatial structure of the target house are connected in series to obtain the polygonal outline of the target house.

[0213] That is, first get the initial polygon outline, such as Figure 8 Figure A is the initial polygon outline before optimization, including many small points and lines. These points and lines may cause the entire polygon area to be small and irregular.

[0214] Then, any two vertices of the to-be-segmented graphs in the polygonal outline that meet the first preset merging condition are determined as first target merging vertices, or second target merging vertices are determined from a plurality of vertices in the polygonal outline that meet the second preset merging condition.

[0215] As an optional embodiment, the first preset merging condition is intended to merge two similar vertices. For example, if the distance between two vertices is less than a certain threshold (such as 5 cm), the two vertices are determined as first target merging vertices.

[0216] As another optional embodiment, the purpose is to remove some vertices by using collinear vertices. The first preset merging condition is, for example, if a plurality of consecutive vertices are located on the same straight line, then the middle vertex between the first and last vertices is determined as the second target merging vertex.

[0217] Finally, the first target merged vertex or the second target merged vertex is merged to obtain a merged polygonal outline, and the merged polygonal outline is a first house floor plan of the target house.

[0218] For two first target merging vertices that meet the first target merging vertex, for example, the two vertices may be merged into one, and in this way, the number of vertices in the polygonal outline is eventually reduced through cyclic judgment.

[0219] For multiple second target merge vertices that meet the first target merge vertices, deletion processing can be performed, and finally the first and last vertices on a certain straight line are retained.

[0220] After the above-mentioned optimization of merging vertices in the polygonal outline, the number of vertices in the polygonal outline can be greatly reduced, making the entire polygonal outline look more regular between line segments and spaces, as shown in FIG. Figure 8 Figure B shows the polygon outline after optimization.

[0221] In the disclosed embodiment, after the vertex merging optimization is performed, the vertex positions can be further optimized and adjusted so that the approximately horizontal or vertical line segments are strictly horizontal and vertical, and the line segments that are approximately parallel or perpendicular to each other remain parallel or perpendicular, while the line segment positions remain near their respective corresponding wall support point clouds.

[0222] Step 1024, determining the window structure of the target house based on the first house floor plan diagram of the target house, and obtaining a second house floor plan diagram, the second house floor plan diagram being the final house floor plan diagram of the target house.

[0223] The inner plane in the wall plane will form a hole at the position of the window (window structure), that is, there is no point cloud data information. In the embodiment of the present disclosure, the spatial information of the window can be restored by finding the point cloud holes on each wall plane, and then the window can be added to the first house floor plan diagram to obtain a complete house system, that is, the second house floor plan diagram.

[0224] Exemplarily, step 1024 may be implemented as follows: Fig. 9 The steps shown are as follows. Fig. 9 As shown, the specific steps include:

[0225] Step 1024a, obtaining point cloud data information corresponding to the edges of each contour in the first house floor plan diagram.

[0226] Step 1024b, projecting the point cloud data information corresponding to the edge of each contour onto the third preset plane to the third preset grid to obtain a third occupancy grid map, wherein the third preset plane is the plane formed by the longitudinal axis and the vertical axis in the point cloud three-dimensional coordinate system.

[0227] Step 1024c: for any edge of a contour, determine in its corresponding third occupancy grid map whether it includes a hole area.

[0228] like Fig.10 As shown, figure A in the figure represents the G area in the third occupancy grid map, that is, the hole area.

[0229] Step 1024d, if it is determined that there is a hole area, a rectangular bounding box of the hole area is determined to obtain the size and coordinate position of the hole area, wherein the hole area is a window structure on the edge of the corresponding outline in the first house floor plan diagram.

[0230] like Fig.10 As shown in the figure, the F area corresponding to the rectangular box in Figure B is the rectangular bounding box of the hole area, and contains information such as the size and coordinate position of the hole area.

[0231] Step 1024e, draw the hollow area on the edge of the corresponding outline in the first house floor plan diagram according to the size and coordinate position of the hollow area, and obtain the second house floor plan diagram.

[0232] Exemplarily, the disclosed embodiment can extract the point cloud near each edge of the optimized polygonal contour, and project the point cloud into the side view of the wall plane to obtain a side view occupancy grid map (i.e., the third occupancy grid map), and extract the rectangular bounding box of the hole area in the third occupancy grid map as the window structure, wherein the horizontal range of the rectangular bounding box in the hole area corresponds to the width of the window structure, and the vertical range corresponds to the height of the window structure. Further, based on the width and height of the window structure, the three-dimensional coordinates of the point cloud can be converted into two-dimensional coordinates to obtain the coordinate position and size range of the window structure on the edge of the corresponding contour in the first house floor plan structure diagram, and draw it to obtain the first house floor plan structure diagram including the window structure, i.e., the second house floor plan structure diagram.

[0233] Step 103, outputting the house floor plan of the target house.

[0234] In some embodiments of the present disclosure, based on the assumption of a fixed thickness of the wall structure of the target house (for example, in mapping, the thickness of the wall structure is generally 20 cm to 40 cm), the contour line of the inner wall plane of the wall structure can be uniformly expanded outward by a certain range (for example, 20 cm to 40 cm) to obtain the position and contour of the contour line of the outer wall plane of the wall structure, so that the obtained house floor plan of the target house includes the contour lines of the inner and outer wall planes. Furthermore, the two endpoints of the line segment corresponding to the window structure are vertically extended outward to intersect with the contour line of the outer wall plane of the wall structure. In this way, the wall structure, space structure, etc. of the house floor plan of the target house are more beautiful. For details, see. Fig.11 In the house floor plan diagram shown in the figure, the four positions pointed to by A, B, C, and D in the figure are the window structure positions of the target house. The internal contour lines in the figure represent the contour lines of the inner wall plane of the wall structure, and the external contour lines in the figure represent the contour lines of the outer wall plane of the wall structure.

[0235] Through the technical solution of the embodiment of the present disclosure, the point cloud data of the target house collected by the point cloud data collection device and the trajectory information of the collection device are obtained, and then the final house floor plan structure diagram of the target house is automatically generated based on the point cloud data information and the trajectory information, and then the house floor plan structure diagram of the target house is output. In the present disclosure, the structural data of the target house is obtained through point cloud data to realize the automatic drawing of the house floor plan structure diagram. Compared with the traditional house measurement, the point cloud data can more objectively reflect the structural parameters of the house, without relying on the professionalism and experience of the surveyor, thereby improving the accuracy of the house measurement and realizing a more accurate house floor plan structure diagram.

[0236] Correspondingly, the embodiments of the present disclosure also provide device embodiments corresponding to the aforementioned method embodiments. Fig.12 FIG. 1 is a structural block diagram of a device for generating a house floor plan diagram according to an embodiment of the device disclosed herein. Fig.12 As shown, a device for generating a house floor plan diagram according to an embodiment of the present disclosure may include a data acquisition module 1201, a house floor plan diagram determination module 1202, and a house floor plan diagram output module 1203. Among them:

[0237] The data acquisition module 1201 is used to acquire first point cloud data information of a target house and track information of a device collecting the first point cloud data;

[0238] A house type structure diagram determining module 1202 is used to determine a house type structure diagram of the target house according to the first point cloud data information and trajectory information;

[0239] The house floor plan outputting module 1203 is used to output the house floor plan of the target house.

[0240] Through the technical solution of the embodiment of the present disclosure, the point cloud data of the target house collected by the point cloud data collection device and the trajectory information of the collection device are obtained, and then the house floor plan structure diagram of the target house is automatically generated based on the point cloud data information and the trajectory information, and then the house floor plan structure diagram of the target house is output. In the present disclosure, the structural data of the target house is obtained through point cloud data to realize the automatic drawing of the house floor plan structure diagram. Compared with the traditional house measurement, the point cloud data can more objectively reflect the structural parameters of the house, without relying on the professionalism and experience of the surveyor, thereby improving the accuracy of the house measurement and realizing a more accurate house floor plan structure diagram.

[0241] exist Fig.12 Based on the illustrated embodiment, another device embodiment is provided. Fig.13 FIG. 1 is a structural block diagram of a device for generating a house floor plan diagram according to another embodiment of the device disclosed herein. Fig.13As shown, a device for generating a house floor plan diagram according to an embodiment of the present disclosure may further include:

[0242] The housing floor plan structure diagram determination module 1202 may include: a denoising processing unit 1202a, a first determination unit 1202b, a second determination unit 1202c and a third determination unit 1202d. Among them:

[0243] The denoising processing unit 1202a is used to perform denoising processing on the first point cloud data information to obtain second point cloud data information.

[0244] As an optional embodiment, the denoising processing unit 1202a may further include:

[0245] The first noise removal subunit is used to remove the first noise point cloud in the first point cloud data information by using a preset point cloud dynamic object removal algorithm to obtain the second point cloud data information, wherein the first noise point cloud is dynamic object noise information.

[0246] As another optional embodiment, the denoising processing unit 1202a may further include:

[0247] A mirror plane determination subunit, used to determine the coordinate range of the mirror plane from the first point cloud data information;

[0248] A first noise point cloud determining unit, configured to determine the point cloud data information in the first point cloud data information and in the coordinate range of the mirror plane as a second noise point cloud;

[0249] The second noise removal subunit deletes the second noise point cloud from the first point cloud data information to obtain the second point cloud data information.

[0250] As another optional embodiment, the denoising processing unit 1202a may further include:

[0251] A first projection subunit projects the first point cloud data information onto a first preset plane into a preset grid to obtain a first occupancy grid map, wherein the first preset plane is a plane formed by a horizontal axis and a vertical axis in a point cloud three-dimensional coordinate system;

[0252] A second projection subunit is used to project the trajectory information of the acquisition device of the first point cloud data onto a first preset plane to obtain two-dimensional trajectory information of the acquisition device on the first preset plane;

[0253] a connected domain determining subunit, configured to determine all connected domains formed by the first point cloud data information according to the first occupancy grid map;

[0254] A noise connected domain determining subunit, configured to determine a noise connected domain from all connected domains according to the two-dimensional trajectory information of the acquisition device on the first preset plane;

[0255] A second noise point cloud determining unit, configured to determine the point cloud data information in the first point cloud data information and in the noise connected domain as a third noise point cloud;

[0256] The third noise removal subunit is used to delete the third noise point cloud from the first point cloud data information to obtain second point cloud data information.

[0257] As another optional embodiment, the denoising processing unit 1202a may further include:

[0258] The second projection subunit is used to adjust the normal of the first point cloud data information according to the first average normal distribution of the first point cloud data information before projecting the first point cloud data information onto the first preset plane into the first preset grid to obtain a first occupancy grid map, so as to obtain a second average normal distribution of the first point cloud data information, wherein the second average normal distribution of the first point cloud data information is perpendicular to the first preset plane.

[0259] As another optional embodiment, the denoising processing unit 1202a may further include:

[0260] A first wall surface determination subunit, configured to determine two planes of any wall of the target house according to the first point cloud data information;

[0261] A third noise point cloud determining unit, configured to determine point cloud data information in the first point cloud data information and between two planes of any wall of the target house as a fourth noise point cloud;

[0262] The fourth noise removal subunit is used to delete the fourth noise point cloud from the first point cloud data information to obtain second point cloud data information.

[0263] The first determining unit 1202b is used to determine the spatial structure information of the target house based on the second point cloud data information.

[0264] As an optional embodiment, the first determining unit 1202b may include:

[0265] A second wall surface determination subunit, configured to determine a wall plane of the target house based on the second point cloud data information;

[0266] A rectangular outline determining subunit, configured to determine a first outer rectangular outline according to an outer bounding box of the second point cloud data information;

[0267] The spatial structure determination subunit is used to determine the spatial structure information of the target house by using the wall plane and the first outer rectangular outline.

[0268] Optionally, the second wall determination subunit can be further used to: determine all spatial planes of the target house based on the second point cloud data information using the regional growing method and the regional average normal; determine a vertical plane from all spatial planes of the target house using a preset plane equation; project the second point cloud data information to the second preset plane to a second preset grid to obtain a second occupancy grid map, wherein the second preset plane is a plane formed by the horizontal axis and the vertical axis in the point cloud three-dimensional coordinate system; delete the vertical plane located inside the second occupancy grid map to obtain a vertical plane located at the edge of the second occupancy grid map; determine the vertical plane located at the edge of the second occupancy grid map as the wall plane of the target house.

[0269] Optionally, the spatial structure determination subunit can be further used to: expand and adjust the outline size of the first outer rectangular outline to obtain a second outer rectangular outline; project the point cloud data information of the wall plane onto the second preset plane to obtain the wall line segments of the wall plane; and use the wall line segments of the wall plane and the second outer rectangular outline to determine the spatial structure information of the target house.

[0270] Furthermore, the spatial structure determination subunit uses the wall line segments of the wall plane and the second outer rectangular outline to determine the spatial structure information of the target house, which can be specifically implemented as follows: according to the position coordinates of the wall line segments of the wall plane, the wall line segments are added to the corresponding coordinate positions of the second outer rectangular outline to obtain the initial spatial structure of the target house; each wall line segment in the initial spatial structure of the target house is supplemented to obtain the supplemented wall line segments; and the final spatial structure formed by the supplemented wall line segments and the second outer rectangular outline is determined as the spatial structure information of the target house.

[0271] The second determining unit 1202c is used to determine the inner and outer contour information of the target house based on the spatial structure information of the target house, so as to obtain a first house floor plan of the target house;

[0272] As an optional embodiment, the second determining unit 1202c may include:

[0273] A graph-to-be-segmented determining subunit is used to determine a corresponding graph to be segmented based on the spatial structural information of the target house, wherein the vertices of the graph to be segmented are polygons formed by the spatial structural information of the target house, and the edges of the graph to be segmented include common edges of adjacent polygons in the polygons formed by the spatial structural information of the target house and edges formed by connecting the vertices of the graph to be segmented with the source points and sink points of the graph cut respectively;

[0274] A weight determination subunit, used to determine the weight of each edge in the graph to be segmented according to the vertices of the graph to be segmented, the edges of the graph to be segmented, the graph cut source points and the sink points;

[0275] A minimum cut determination subunit is used to determine the minimum cut of the graph to be segmented based on the weight calculation of each edge in the graph to be segmented using a preset graph cut algorithm, so as to obtain the inner boundary and the outer boundary of the spatial structure of the target house;

[0276] The connection subunit is used to connect the inner boundary and the outer boundary of the spatial structure of the target house in series to obtain a first house floor plan diagram of the target house.

[0277] Optionally, the connection subunit connects in series the edges of the inner boundary and the outer boundary of the spatial structure of the target house to obtain a first house floor plan diagram of the target house, which can be specifically implemented as follows: connecting in series the edges of the inner boundary and the outer boundary of the spatial structure of the target house to obtain a polygonal outline of the target house; determining any two vertices of the to-be-segmented graph in the polygonal outline that satisfy a first preset merging condition as first target merging vertices, or determining a second target merging vertex from multiple vertices in the polygonal outline that satisfy a second preset merging condition; merging the first target merging vertex or the second target merging vertex to obtain a merged polygonal outline, and the merged polygonal outline is the first house floor plan diagram of the target house.

[0278] The third determining unit 1202d is used to determine the window structure of the target house based on the first house floor plan diagram of the target house, and obtain a second house floor plan diagram, where the second house floor plan diagram is the final house floor plan diagram of the target house.

[0279] As an optional embodiment, the third determining unit 1202d may include:

[0280] A contour point cloud acquisition subunit, configured to acquire point cloud data information corresponding to edges of each contour in the first house floor plan diagram based on the first house floor plan diagram of the target house;

[0281] A third projection subunit is used to project the point cloud data information corresponding to the edge of each contour onto a third preset plane and into a third preset grid to obtain a third occupancy grid map, wherein the third preset plane is a plane formed by the longitudinal axis and the vertical axis in the point cloud three-dimensional coordinate system;

[0282] A hole region determination subunit, used for determining, for an edge of any contour, whether a hole region is included in the third occupancy grid map corresponding to the edge of any contour;

[0283] a window structure determination subunit, for determining a rectangular bounding box of the hole area if it is determined that the hole area is included, and obtaining the size and coordinate position of the hole area, wherein the hole area is a window structure on the edge of the corresponding outline in the first house floor plan;

[0284] The form drawing subunit is used to draw the hollow area on the edge of the corresponding outline in the first house floor plan according to the size and coordinate position of the hollow area to obtain the second house floor plan.

[0285] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0286] Below, reference Fig.14 The electronic device according to the embodiment of the present disclosure is described. The electronic device may be any one or both of the first device and the second device, or a stand-alone device independent of them, and the stand-alone device may communicate with the first device and the second device to receive the collected input signals from them.

[0287] Fig.14 A block diagram of an electronic device according to an embodiment of the present disclosure is illustrated.

[0288] like Fig.14 As shown, the electronic device includes one or more processors and memory.

[0289] The processor may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0290] The memory may store one or more computer program products, and the memory may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, a random access memory (RAM) and / or a cache memory (cache), etc. The non-volatile memory may include, for example, a read-only memory (ROM), a hard disk, a flash memory, etc. One or more computer program products may be stored on the computer-readable storage medium, and the processor may run the computer program product to implement the above-described method for generating a house floor plan diagram of each embodiment of the present disclosure and / or other desired functions.

[0291] In one example, the electronic device may further include: an input device and an output device, and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0292] In addition, the input device may also include, for example, a keyboard, a mouse, and the like.

[0293] The output device can output various information to the outside, including determined distance information, direction information, etc. The output device can include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto, and the like.

[0294] Of course, to simplify, Fig.14 Only some of the components related to the present disclosure in the electronic device are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, according to specific application situations, the electronic device may further include any other appropriate components.

[0295] In addition to the above-mentioned methods and devices, an embodiment of the present disclosure may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps in the method for generating a house floor plan diagram according to various embodiments of the present disclosure described in the above part of this specification.

[0296] The computer program product may be written in any combination of one or more programming languages ​​to write program code for performing the operations of the disclosed embodiments, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0297] In addition, an embodiment of the present disclosure may also be a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the processor executes the steps in the method for generating a house floor plan diagram according to various embodiments of the present disclosure described in the above part of this specification.

[0298] The computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0299] The basic principles of the present disclosure are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present disclosure are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present disclosure. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, and are not limiting. The above details do not limit the present disclosure to the necessity of adopting the above specific details to be implemented.

[0300] Each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0301] The block diagrams of the devices, apparatuses, equipment, and systems involved in this disclosure are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagrams. As will be appreciated by those skilled in the art, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including," "comprising," "having," and the like are open words, referring to "including but not limited to," and can be used interchangeably therewith. The words "or" and "and" used herein refer to the words "and / or," and can be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to," and can be used interchangeably therewith.

[0302] The method and apparatus of the present disclosure may be implemented in many ways. For example, the method and apparatus of the present disclosure may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of steps for the method is for illustration only, and the steps of the method of the present disclosure are not limited to the order specifically described above, unless otherwise specifically stated. In addition, in some embodiments, the present disclosure may also be implemented as a program recorded in a recording medium, which includes machine-readable instructions for implementing the method according to the present disclosure. Therefore, the present disclosure also covers a recording medium storing a program for executing the method according to the present disclosure.

[0303] It should also be noted that in the apparatus, device and method of the present disclosure, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present disclosure.

[0304] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0305] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present disclosure to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A method for generating a house type structure diagram, characterized in that: The method comprises: Acquire first point cloud data information of a target house and trajectory information of a device collecting the first point cloud data; Determine a house floor plan of the target house according to the first point cloud data information and trajectory information; Output the house floor plan of the target house.

2. The method according to claim 1, characterized in that The step of determining the house floor plan of the target house according to the first point cloud data information and the trajectory information includes: Performing denoising processing on the first point cloud data information to obtain second point cloud data information; Determine the spatial structure information of the target house based on the second point cloud data information; Determine the inner and outer contour information of the target house based on the spatial structure information of the target house, and obtain a first house floor plan of the target house; The window structure of the target house is determined based on the first house floor plan diagram of the target house to obtain a second house floor plan diagram, wherein the second house floor plan diagram is the final house floor plan diagram of the target house.

3. The method according to claim 2, characterized in that The step of performing denoising on the first point cloud data information to obtain second point cloud data information includes: Using a preset point cloud dynamic object removal algorithm to remove a first noise point cloud in the first point cloud data information to obtain the second point cloud data information, wherein the first noise point cloud is dynamic object noise information; and / or, Determining a coordinate range of a mirror plane from the first point cloud data information; Determine the point cloud data information in the first point cloud data information and in the coordinate range of the mirror plane as a second noise point cloud; Deleting the second noise point cloud from the first point cloud data information to obtain the second point cloud data information; and / or, Projecting the first point cloud data information onto a first preset plane and into a preset grid to obtain a first occupancy grid map, wherein the first preset plane is a plane formed by a horizontal axis and a vertical axis in a point cloud three-dimensional coordinate system; Projecting the trajectory information of the acquisition device of the first point cloud data onto a first preset plane to obtain two-dimensional trajectory information of the acquisition device on the first preset plane; Determine all connected domains formed by the first point cloud data information according to the first occupancy grid map; Determining a noise connected domain from all the connected domains according to the two-dimensional trajectory information of the acquisition device on the first preset plane; Determine the point cloud data information in the first point cloud data information and in the noise connected domain as a third noise point cloud; Deleting the third noise point cloud from the first point cloud data information to obtain second point cloud data information; and / or, Before projecting the first point cloud data information onto a first preset plane and into a first preset grid to obtain a first occupancy grid map, adjusting the normal of the first point cloud data information according to a first average normal distribution of the first point cloud data information to obtain a second average normal distribution of the first point cloud data information, wherein the second average normal distribution of the first point cloud data information is perpendicular to the first preset plane; and / or, Determine two planes of any wall of the target house according to the first point cloud data information; Determine the point cloud data information in the first point cloud data information and between two planes of any wall of the target house as a fourth noise point cloud; The fourth noise point cloud is deleted from the first point cloud data information to obtain second point cloud data information.

4. The method according to claim 2, characterized in that: The determining the spatial structure information of the target house based on the second point cloud data information includes: Determining a wall plane of the target house based on the second point cloud data information; Determine a first outer rectangular contour according to an outer bounding box of the second point cloud data information; The spatial structural information of the target house is determined using the wall plane and the first outer rectangular outline.

5. The method according to claim 4, characterized in that The determining the wall plane of the target house based on the second point cloud data information includes: Determine all spatial planes of the target house based on the second point cloud data information by using a region growing method and a regional average normal; Determine a vertical plane from all spatial planes of the target house using a preset plane equation; Projecting the second point cloud data information onto a second preset plane and into a second preset grid to obtain a second occupancy grid map, wherein the second preset plane is a plane formed by a horizontal axis and a vertical axis in a point cloud three-dimensional coordinate system; Deleting the vertical plane located inside the second occupancy grid map to obtain a vertical plane located at an edge of the second occupancy grid map; The vertical plane located at the edge of the second occupancy grid map is determined as the wall plane of the target house.

6. The method according to claim 4 or 5, characterized in that: The determining of the spatial structural information of the target house by using the wall plane and the first outer rectangular outline includes: Enlarging and adjusting the outline size of the first outer rectangular outline to obtain a second outer rectangular outline; Projecting the point cloud data information of the wall plane onto the second preset plane to obtain the wall line segments of the wall plane; The spatial structural information of the target house is determined using the wall line segments of the wall plane and the second outer rectangular outline.

7. The method according to claim 6, characterized in that The determining of the spatial structural information of the target house by using the wall line segments of the wall plane and the second outer rectangular outline includes: According to the position coordinates of the wall line segments of the wall plane, the wall line segments are added to the corresponding coordinate positions of the second outer rectangular contour to obtain the initial spatial structure of the target house; Supplementing each wall line segment in the initial spatial structure of the target house to obtain supplemented wall line segments; The final spatial structure formed by the supplemented wall line segments and the second outer rectangular outline is determined as the spatial structure information of the target house.

8. The method according to claim 2, characterized in that: The step of determining the inner and outer contour information of the target house based on the spatial structure information of the target house to obtain a first house floor plan diagram of the target house includes: Determine a corresponding graph to be segmented based on the spatial structure information of the target house, wherein the vertices of the graph to be segmented are polygons formed by the spatial structure information of the target house, and the edges of the graph to be segmented include common edges of adjacent polygons in the polygons formed by the spatial structure information of the target house and edges formed by connecting the vertices of the graph to be segmented with the source points and the sink points of the graph cut respectively; Determine the weight of each edge in the graph to be segmented according to the vertices of the graph to be segmented, the edges of the graph to be segmented, the graph cut source points and the sink points; Determine the minimum cut of the graph to be segmented by using a preset graph cut algorithm based on the weight calculation of each edge in the graph to be segmented, and obtain the inner boundary and the outer boundary of the spatial structure of the target house; The inner boundary and the outer boundary edges of the spatial structure of the target house are connected in series to obtain a first house floor plan diagram of the target house.

9. The method according to claim 8, characterized in that The step of connecting the inner boundary and the outer boundary of the spatial structure of the target house in series to obtain a first house floor plan diagram of the target house comprises: Connecting the inner boundary and the outer boundary of the spatial structure of the target house in series to obtain a polygonal outline of the target house; Determine any two vertices of the to-be-segmented graph in the polygonal outline that meet a first preset merging condition as first target merging vertices, or determine a second target merging vertex from a plurality of vertices in the polygonal outline that meet a second preset merging condition; The first target merged vertex or the second target merged vertex is merged to obtain a merged polygonal outline, where the merged polygonal outline is a first house floor plan of the target house.

10. The method according to any one of claims 2, 8 or 9, characterized in that: The window structure of the target house is determined based on the first house floor plan diagram of the target house to obtain a second house floor plan diagram, wherein the second house floor plan diagram is the final house floor plan diagram of the target house, including: Obtaining point cloud data information corresponding to the edges of each contour in the first house layout diagram; Projecting the point cloud data information corresponding to the edge of each contour onto a third preset plane and a third preset grid to obtain a third occupancy grid map, wherein the third preset plane is a plane formed by the longitudinal axis and the vertical axis in the point cloud three-dimensional coordinate system; For any edge of a contour, determining in the corresponding third occupancy grid map whether it includes a hole area; If it is determined that the hole area is included, a rectangular bounding box of the hole area is determined to obtain the size and coordinate position of the hole area, and the hole area is a window structure on the edge of the corresponding outline in the first house floor plan; The hollow area is drawn on the edge of the corresponding outline in the first house floor plan diagram according to the size and coordinate position of the hollow area to obtain the second house floor plan diagram.

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