Precise matching method, laser radar and system for point cloud data

Abstract

The present invention discloses a precise matching method, laser radar, and system for point cloud data. The precise matching method comprises: scanning a target area to obtain point cloud data, obtaining floor plan data for the target area; extracting contour data from the floor plan data and obtaining concave-convex point data based on the contour data; obtaining a point cloud plan and wall intersection data from the point cloud plan based on the point cloud data; matching the concave-convex point data with the wall intersection data; and obtaining a correspondence between the floor plan data and the point cloud plan. The present invention can achieve correspondence between floor plans and point cloud data, aligning measured data with engineering drawings, and facilitating floor measurement and further calculation and processing of the measured data.

Description

Technical Field

[0001] The present invention relates to a precise matching method, laser radar and system for point cloud data. Background Art

[0002] 3D laser scanners suitable for indoor scanning often use ToF (Time-of-flight) ranging technology, with LiDAR (light detection and ranging) being the most commonly used. This optical remote sensing technology calculates the distance to an object by measuring the time difference between transmitted and received pulse signals. While its advantages lie in high accuracy and long range, it is also limited by the physical properties of light. For example, LiDAR has difficulty measuring the distance from the sensor to a window (light refracts when passing through glass) and cannot scan structures behind walls (light travels in a straight line). Furthermore, LiDAR's accuracy is affected by the object's material, its distance from the scanner, and the angle of incidence.

[0003] 3D laser scanners suitable for indoor environment scanning are primarily categorized as handheld and fixed. Handheld devices are lightweight and portable, and feature built-in self-positioning capabilities, allowing operators to move them while scanning, making this technology less susceptible to occlusion. Fixed scanners, supported by a tripod, rotate their base to collect data from a 360° field of view, encompassing the beam's sphere centered at that point. Due to occlusion, operators often need to perform multiple scans in different locations, stitching and rescanning together to capture all the details of the space.

[0004] Actual measurement refers to a method that uses measurement tools to conduct on-site testing and measurement to accurately reflect product quality data. According to relevant quality acceptance standards, the error in measurement control engineering quality data is within the range allowed by national housing construction standards.

[0005] One of the most important purposes of point clouds captured by various 3D scanners is to digitize reality, transforming real-world space into virtual world information. This allows for the completion of numerous different measurement and surveying tasks within the virtual world, which is the foundation of digital twins. While various measurement and surveying tasks can be performed in the virtual world, this valuable information must be reflected in the physical world to be most effectively utilized. In construction, one of the most commonly used bridges between the virtual and physical worlds is the floor plan. Floor plans are widely used in the industry and serve as a reference for every work procedure. Therefore, accurately positioning scanned point clouds on floor plans is extremely useful for bridging the virtual and physical worlds.

[0006] In the existing technology, floor plans and digital point cloud models cannot be accurately matched and corresponded. Summary of the Invention

[0007] The technical problem to be solved by the present invention is to overcome the defect in the prior art that floor plans and digital point cloud models cannot be accurately matched and corresponded, and to provide a method, a laser radar and a system for accurately matching point cloud data that can achieve the correspondence between floor plans and point cloud data, correspond the measured data with engineering drawings, facilitate the measurement of work floors and the further calculation and processing of the measured data.

[0008] The present invention solves the above technical problems through the following technical solutions:

[0009] A precise matching method for point cloud data, the precise matching method comprising:

[0010] Scan the area to be tested to obtain point cloud data and obtain floor plan data of the area to be tested;

[0011] Extracting contour data from the floor plan data and obtaining concave-convex point data based on the contour data, and obtaining a point cloud plan and wall intersection data in the point cloud plan based on the point cloud data;

[0012] Match the concave and convex point data with the wall intersection data;

[0013] Get the correspondence between floor plan data and point cloud floor plan.

[0014] Preferably, the step of obtaining a point cloud plan view and wall intersection data in the point cloud plan view based on the point cloud data includes:

[0015] Obtaining the point cloud plan view using a top view of the point cloud data or by returning the height coordinates to zero;

[0016] Contour data in the point cloud plan is extracted, and wall intersection data is acquired based on the contour data of the point cloud plan.

[0017] Preferably, the step of obtaining a point cloud plan view and wall intersection data in the point cloud plan view based on the point cloud data includes:

[0018] Obtaining the point cloud plan view using a top view of the point cloud data or by returning the height coordinates to zero;

[0019] Get the wall model from the point cloud data;

[0020] Get the intersection position of adjacent wall models;

[0021] The wall intersection data is obtained based on the projection of the intersection position in the point cloud plan.

[0022] Preferably, matching the concave-convex point data with the wall intersection data includes:

[0023] Match the directions of the floor plan data and the point cloud plan, and then fix the positions of the floor plan data and the point cloud plan after the matching directions;

[0024] For a target area in the floor plan data, the matching distance from the concave-convex point in the target area to the wall intersection point in the matching area of ​​the point cloud data is calculated using the concave-convex point data and the wall intersection point data;

[0025] Determine whether the matching distance from each concave-convex point in the target area to the corresponding wall intersection point in the matching area meets the preset matching length. If so, complete the matching of the concave-convex point data in the target area with the wall intersection data in the matching area.

[0026] Preferably, before completing the matching of the concave-convex point data in the target area with the wall intersection data in the matching area, the method includes:

[0027] For a target area in the floor plan data, find the number of matching points between the concave and convex points and the wall intersections in the matching area within the threshold distance tolerance;

[0028] It is determined whether the number of matches between the concave-convex points in the target area and the wall intersection points in the matching area meets a preset value. If so, the matching of the concave-convex point data in the target area and the wall intersection data in the matching area is completed.

[0029] Preferably, before completing the matching of the concave-convex point data in the target area with the wall intersection data in the matching area, the method includes:

[0030] For a target area in the floor plan data, calculate the ground overlap ratio between the target area and the matching area;

[0031] It is determined whether the overlap ratio meets the preset ratio. If so, the concave-convex point data in the target area is matched with the wall intersection data in the matching area.

[0032] Preferably, the matching distance, the number of matches, and the bottom surface overlap ratio all correspond to a weight value, and the precise matching method includes:

[0033] Obtain a matching score using matching distance, matching number, ground overlap ratio, and weight value;

[0034] The concave-convex point data is matched with the wall intersection point data using the matching score.

[0035] Preferably, the exact matching method includes:

[0036] Segmenting the floor plan and the point cloud plan in the floor plan data to obtain room areas in units of rooms;

[0037] Extracting contour data of the room area of ​​the floor plan data and obtaining concave-convex point data based on the contour data, and obtaining wall intersection data of the room area of ​​the point cloud data;

[0038] For a target area, initial matching is performed using the area to obtain several initial areas, wherein the target area is the room area of ​​the floor plan data, and the initial area is the room area of ​​the point cloud data;

[0039] Use the ground overlap ratio to match the initial area to obtain the matching area;

[0040] Select a starting concave-convex point according to the plane coordinates of the target area, and obtain the wall intersection point corresponding to the matching area;

[0041] Starting from the starting concave-convex point, the distances from the concave-convex points to the corresponding wall intersection points are calculated in sequence;

[0042] It is determined whether the distance between each concave-convex point in the target area and the corresponding wall intersection point in the matching area meets the preset distance. If so, the matching of the concave-convex point data in the target area and the wall intersection point data in the matching area is completed.

[0043] The present invention also provides a laser radar, which is used to implement the precise matching method as described above.

[0044] The present invention also provides a laser radar system, which includes a processing module and a laser radar. The processing module is used to implement the precise matching method as described above, and the laser radar is used to obtain point cloud data.

[0045] Based on the common sense in this field, the above-mentioned preferred conditions can be arbitrarily combined to obtain the preferred embodiments of the present invention.

[0046] The positive progress effect of the present invention is:

[0047] The precise matching method, laser radar and system for point cloud data of the present invention can achieve the correspondence between floor plans and point cloud data, so that measured data can be matched with engineering drawings, facilitating floor measurement and further calculation and processing of measurement data. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a structural diagram of matching floor plan data and point cloud plan in Example 1 of the present invention.

[0049] Figure 2 This is a flowchart of the precise matching method according to embodiment 1 of the present invention. DETAILED DESCRIPTION

[0050] The present invention is further described below by way of examples, but the present invention is not limited to the scope of the examples.

[0051] Example 1

[0052] This embodiment provides a laser radar system, which includes a laser radar and a processing module. The processing module can be an intelligent terminal, a server, or a processor equipped in the laser radar itself.

[0053] In this embodiment, the processing module is a smart terminal or a server.

[0054] In other embodiments, the laser radar may also be a 3D scanning robot, and the laser radar includes the processing module.

[0055] In this embodiment, the processing module is used to scan the area to be measured by a laser radar to obtain point cloud data, and obtain the floor plan data of the area to be measured through a network, a scanning device, a USB flash drive, etc.;

[0056] The processing module is further configured to:

[0057] Extracting contour data from the floor plan data and obtaining concave-convex point data based on the contour data, and obtaining a point cloud plan and wall intersection data in the point cloud plan based on the point cloud data;

[0058] Match the concave and convex point data with the wall intersection data;

[0059] Get the correspondence between floor plan data and point cloud floor plan.

[0060] The concave-convex point data includes the position coordinates, numbers, and quantity of the concave-convex point. The concave-convex point refers to the actual corner of the wall.

[0061] The wall intersection data includes information such as the coordinates, numbers, and quantities of the wall intersections.

[0062] In the correspondence relationship, the floor plan data and the point cloud data correspond to each other, and the concave and convex points can correspond one to one with the intersection points of the wall. Specifically, the two-dimensional coordinates in the floor plan can be matched with the three-dimensional coordinates of the point cloud data in the plane, and the corresponding points can correspond to the wall.

[0063] Furthermore, the processing module is used to:

[0064] Obtaining the point cloud plan view using a top view of the point cloud data or by returning the height coordinates to zero;

[0065] Contour data in the point cloud plan is extracted, and wall intersection data is acquired based on the contour data of the point cloud plan.

[0066] Specifically, the processing module is used to:

[0067] Obtaining the point cloud plan view using a top view of the point cloud data or by returning the height coordinates to zero;

[0068] Get the wall model from the point cloud data;

[0069] Get the intersection position of adjacent wall models;

[0070] The wall intersection data is obtained based on the projection of the intersection position in the point cloud plan.

[0071] This embodiment provides three evaluation criteria for matching, and the processing module is used to:

[0072] Match the directions of the floor plan data and the point cloud plan, and then fix the positions of the floor plan data and the point cloud plan after the matching directions;

[0073] For a target area in the floor plan data, the matching distance from the concave-convex point in the target area to the wall intersection point in the matching area of ​​the point cloud data is calculated using the concave-convex point data and the wall intersection point data;

[0074] Determine whether the matching distance from each concave-convex point in the target area to the corresponding wall intersection point in the matching area meets the preset matching length. If so, complete the matching of the concave-convex point data in the target area with the wall intersection data in the matching area.

[0075] See also Figure 1 After matching the directions of the floor plan data 11 and the point cloud plan (the reference numeral 21 of the point cloud plan), the distance between the concave and convex point 111 and the corresponding wall intersection 211 is approximately the matching distance. If the distance between each concave and convex point and the corresponding wall intersection is approximately the matching distance, the matching success rate is high.

[0076] Furthermore, the processing module is used to:

[0077] For a target area in the floor plan data, find the number of matching points between the concave and convex points and the wall intersections in the matching area within the threshold distance tolerance;

[0078] It is determined whether the number of matches between the concave-convex points in the target area and the wall intersection points in the matching area meets a preset value. If so, the matching of the concave-convex point data in the target area and the wall intersection data in the matching area is completed.

[0079] At the same time, in order to improve the matching speed, you can first perform an initial match based on the number of matches, and then calculate the matching distance.

[0080] Furthermore, the processing module is used to:

[0081] For a target area in the floor plan data, calculate the ground overlap ratio between the target area and the matching area;

[0082] It is determined whether the overlap ratio meets the preset ratio. If so, the concave-convex point data in the target area is matched with the wall intersection data in the matching area.

[0083] In order to improve the matching speed, the initial matching can be performed first by the ground overlap ratio, and then the matching distance is calculated.

[0084] Using big data analysis, the processing module of this embodiment is used to:

[0085] Obtain a matching score using matching distance, matching number, ground overlap ratio, and weight value;

[0086] The concave-convex point data is matched with the wall intersection point data using the matching score.

[0087] See also Figure 2 , using the above-mentioned laser radar system, this embodiment further provides a precise matching method, including:

[0088] Step 100: Scan the area to be measured to obtain point cloud data;

[0089] Step 101: Obtain floor plan data of the area to be tested;

[0090] Steps 100 and 101 may be performed simultaneously.

[0091] Step 102: extracting contour data from the floor plan data and obtaining concave-convex point data based on the contour data;

[0092] Step 103: Obtain a point cloud plan view and wall intersection data in the point cloud plan view based on the point cloud data;

[0093] Steps 102 and 103 may be performed simultaneously.

[0094] Step 104: Match the concave-convex point data with the wall intersection data;

[0095] Step 105: Obtain the correspondence between the floor plan data and the point cloud plan.

[0096] Wherein, step 103 specifically includes:

[0097] Obtaining the point cloud plan view using a top view of the point cloud data or by returning the height coordinates to zero;

[0098] Contour data in the point cloud plan is extracted, and wall intersection data is acquired based on the contour data of the point cloud plan.

[0099] Specifically, step 103 includes:

[0100] Obtaining the point cloud plan view using a top view of the point cloud data or by returning the height coordinates to zero;

[0101] Get the wall model from the point cloud data;

[0102] Get the intersection position of adjacent wall models;

[0103] The wall intersection data is obtained based on the projection of the intersection position in the point cloud plan.

[0104] Furthermore, step 104 specifically includes:

[0105] Match the directions of the floor plan data and the point cloud plan, and then fix the positions of the floor plan data and the point cloud plan after the matching directions;

[0106] For a target area in the floor plan data, the matching distance from the concave-convex point in the target area to the wall intersection point in the matching area of ​​the point cloud data is calculated using the concave-convex point data and the wall intersection point data;

[0107] Determine whether the matching distance from each concave-convex point in the target area to the corresponding wall intersection point in the matching area meets the preset matching length. If so, complete the matching of the concave-convex point data in the target area with the wall intersection data in the matching area.

[0108] Before completing the matching of the concave-convex point data in the target area with the wall intersection data in the matching area, the method includes:

[0109] For a target area in the floor plan data, find the number of matching points between the concave and convex points and the wall intersections in the matching area within the threshold distance tolerance;

[0110] It is determined whether the number of matches between the concave-convex points in the target area and the wall intersection points in the matching area meets a preset value. If so, the matching of the concave-convex point data in the target area and the wall intersection data in the matching area is completed.

[0111] Furthermore, before completing the matching of the concave-convex point data in the target area with the wall intersection data in the matching area, the method further includes:

[0112] For a target area in the floor plan data, calculate the ground overlap ratio between the target area and the matching area;

[0113] It is determined whether the overlap ratio meets the preset ratio. If so, the concave-convex point data in the target area is matched with the wall intersection data in the matching area.

[0114] The matching distance, the number of matches, and the bottom surface overlap ratio all correspond to a weight value, and the precise matching method includes:

[0115] Obtain a matching score using matching distance, matching number, ground overlap ratio, and weight value;

[0116] The concave-convex point data is matched with the wall intersection point data using the matching score.

[0117] The precise matching method, laser radar and system for point cloud data of this embodiment can achieve the correspondence between floor plans and point cloud data, so that the measured data can be matched with engineering drawings, facilitating the measurement of work floors and the further calculation and processing of the measured data.

[0118] Example 2

[0119] This embodiment is basically the same as the first embodiment, except that:

[0120] The processing module is used for:

[0121] Segmenting the floor plan and the point cloud plan in the floor plan data to obtain room areas in units of rooms;

[0122] Extracting contour data of the room area of ​​the floor plan data and obtaining concave-convex point data based on the contour data, and obtaining wall intersection data of the room area of ​​the point cloud data;

[0123] For a target area, initial matching is performed using the area to obtain several initial areas, wherein the target area is the room area of ​​the floor plan data, and the initial area is the room area of ​​the point cloud data;

[0124] Use the ground overlap ratio to match the initial area to obtain the matching area;

[0125] Select a starting concave-convex point according to the plane coordinates of the target area, and obtain the wall intersection point corresponding to the matching area;

[0126] Starting from the starting concave-convex point, the distances from the concave-convex points to the corresponding wall intersection points are calculated in sequence;

[0127] It is determined whether the distance between each concave-convex point in the target area and the corresponding wall intersection point in the matching area meets the preset distance. If so, the matching of the concave-convex point data in the target area and the wall intersection point data in the matching area is completed.

[0128] Correspondingly, the exact matching methods include:

[0129] Segmenting the floor plan and the point cloud plan in the floor plan data to obtain room areas in units of rooms;

[0130] Extracting contour data of the room area of ​​the floor plan data and obtaining concave-convex point data based on the contour data, and obtaining wall intersection data of the room area of ​​the point cloud data;

[0131] Specifically, matching the concave and convex point data with the wall intersection data includes:

[0132] For a target area, initial matching is performed using the area to obtain several initial areas, wherein the target area is the room area of ​​the floor plan data, and the initial area is the room area of ​​the point cloud data;

[0133] Use the ground overlap ratio to match the initial area to obtain the matching area;

[0134] Select a starting concave-convex point according to the plane coordinates of the target area, and obtain the wall intersection point corresponding to the matching area;

[0135] Starting from the starting concave-convex point, the distances from the concave-convex points to the corresponding wall intersection points are calculated in sequence;

[0136] It is determined whether the distance between each concave-convex point in the target area and the corresponding wall intersection point in the matching area meets the preset distance. If so, the matching of the concave-convex point data in the target area and the wall intersection point data in the matching area is completed.

[0137] Although specific embodiments of the present invention have been described above, those skilled in the art will appreciate that these are merely illustrative and that the scope of the present invention is defined by the appended claims. Those skilled in the art may make various changes or modifications to these embodiments without departing from the principles and essence of the present invention, and such changes and modifications are intended to fall within the scope of the present invention.

Claims

1. A precise matching method for point cloud data, characterized in that: The exact matching method includes: Scan the area to be tested to obtain point cloud data and obtain floor plan data of the area to be tested; Extracting contour data from the floor plan data and obtaining concave-convex point data based on the contour data, and obtaining a point cloud plan and wall intersection data in the point cloud plan based on the point cloud data; Match the concave and convex point data with the wall intersection data; Obtain the correspondence between floor plan data and point cloud plan; The matching of the concave-convex point data with the wall intersection data includes: Match the directions of the floor plan data and the point cloud plan, and then fix the positions of the floor plan data and the point cloud plan after the matching directions; For a target area in the floor plan data, the matching distance from the concave-convex point in the target area to the wall intersection point in the matching area of ​​the point cloud data is calculated using the concave-convex point data and the wall intersection point data; Determine whether the matching distance from each concave-convex point in the target area to the corresponding wall intersection point in the matching area meets the preset matching length. If so, complete the matching of the concave-convex point data in the target area with the wall intersection data in the matching area. The exact matching method includes: Segmenting the floor plan and the point cloud plan in the floor plan data to obtain room areas in units of rooms; Extracting contour data of the room area of ​​the floor plan data and obtaining concave-convex point data based on the contour data, and obtaining wall intersection data of the room area of ​​the point cloud data; For a target area, initial matching is performed using the area to obtain several initial areas, wherein the target area is the room area of ​​the floor plan data, and the initial area is the room area of ​​the point cloud data; Use the ground overlap ratio to match the initial area to obtain the matching area; Select a starting concave-convex point according to the plane coordinates of the target area, and obtain the wall intersection point corresponding to the matching area; Starting from the starting concave-convex point, the distances from the concave-convex points to the corresponding wall intersection points are calculated in sequence; It is determined whether the distance between each concave-convex point in the target area and the corresponding wall intersection point in the matching area meets the preset distance. If so, the matching of the concave-convex point data in the target area and the wall intersection point data in the matching area is completed.

2. The precise matching method according to claim 1, wherein: The step of obtaining a point cloud plan and wall intersection data in the point cloud plan based on the point cloud data includes: Obtaining the point cloud plan view using a top view of the point cloud data or by returning the height coordinates to zero; Contour data in the point cloud plan is extracted, and wall intersection data is acquired based on the contour data of the point cloud plan.

3. The precise matching method according to claim 1, wherein: The step of obtaining a point cloud plan and wall intersection data in the point cloud plan based on the point cloud data includes: Obtaining the point cloud plan view using a top view of the point cloud data or by returning the height coordinates to zero; Get the wall model from the point cloud data; Get the intersection position of adjacent wall models; The wall intersection data is obtained based on the projection of the intersection position in the point cloud plan.

4. The precise matching method according to claim 1, wherein: Before completing the matching of the concave-convex point data in the target area and the wall intersection point data in the matching area, the method includes: For a target area in the floor plan data, find the number of matching points between the concave and convex points and the wall intersection points in the matching area within the threshold distance tolerance; It is determined whether the number of matches between the concave-convex points in the target area and the wall intersection points in the matching area meets a preset value. If so, the matching of the concave-convex point data in the target area and the wall intersection data in the matching area is completed.

5. The precise matching method according to claim 4, wherein: Before completing the matching of the concave-convex point data in the target area and the wall intersection point data in the matching area, the method includes: For a target area in the floor plan data, calculate the ground overlap ratio between the target area and the matching area; It is determined whether the overlap ratio meets the preset ratio. If so, the concave-convex point data in the target area is matched with the wall intersection data in the matching area.

6. The precise matching method according to claim 5, wherein: The matching distance, the number of matches, and the bottom surface overlap ratio all correspond to a weight value, and the precise matching method includes: Obtain a matching score using matching distance, matching number, ground overlap ratio, and weight value; The concave-convex point data is matched with the wall intersection point data using the matching score.

7. A laser radar, characterized in that: The laser radar is used to implement the precise matching method as described in any one of claims 1 to 6.

8. A laser radar system, characterized in that: The laser radar system includes a processing module and a laser radar, the processing module is used to implement the precise matching method as described in any one of claims 1 to 6, and the laser radar is used to obtain point cloud data.

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