Indoor 3D barrier-free map generation method based on LiDAR point cloud and BIM collision simulation

Abstract

This invention provides a method for generating indoor 3D barrier-free maps based on LiDAR point clouds and BIM collision simulation, relating to the field of building construction technology. The method includes the following steps: S10, laser point cloud acquisition and preprocessing: laser point cloud acquisition is performed on the indoor space, and multiple frames of the acquired point cloud are preprocessed; S20, point cloud quantity statistics and elevation fitting: vertical point cloud quantity statistics are performed on the preprocessed point cloud; S30, generating a 3D voxel model corresponding to the ground point cloud; S40, passage simulation and collision detection: passage simulation and collision detection are performed to generate multiple passage domains adapted to different access devices. The beneficial effects of this invention are: it aims to solve the problem of difficult route planning for indoor barrier-free passages for special groups, and to construct a 3D indoor barrier-free passage map that can adapt to multiple types of barrier-free assistive access devices.

Description

Technical Field

[0001] The present invention relates to the technical field of building construction, and more specifically, to a method for generating an indoor three-dimensional barrier-free map based on LiDAR point cloud and BIM collision simulation. Background Art

[0002] In the process of building an accessible indoor map, if there is no original CAD drawing of the building for which the indoor map is to be built, staff will need to measure the dimensions of the walls, doors, windows, etc. of each room in the building to obtain the indoor map of the building. At the same time, based on the measurement results, they will further determine accessible indoor routes suitable for special groups such as wheelchair users.

[0003] The process of building this kind of indoor map requires a large number of staff with professional mapping skills to personally survey the indoor environment. This requires high skills and a lot of work from the staff, and it is also impossible to accurately plan a three-dimensional digital map of indoor barrier-free passages. Summary of the invention

[0004] In order to overcome the shortcomings of the prior art, the present invention provides a method for generating an indoor three-dimensional barrier-free map based on LiDAR point cloud and BIM collision simulation.

[0005] The technical solution adopted by the present invention to solve the technical problem is: a method for generating an indoor three-dimensional barrier-free map based on LiDAR point cloud and BIM collision simulation, the improvement of which is that the method comprises the following steps:

[0006] S10, laser point cloud acquisition and preprocessing, performing laser point cloud acquisition on the indoor space and preprocessing the acquired multi-frame point clouds, the preprocessing process includes point cloud noise reduction processing and multi-frame point cloud registration;

[0007] S20, point cloud quantity statistics and elevation fitting, performing point cloud quantity statistics in the vertical direction on the pre-processed point cloud, performing elevation fitting of the floor and ceiling, and detecting the ground point cloud;

[0008] S30, generating a three-dimensional voxel model corresponding to the ground point cloud, extracting a general barrier-free passable area on the three-dimensional voxel model, and segmenting the passable voxel area at the location where the height difference suddenly changes;

[0009] S40, traffic simulation and collision detection, performing traffic simulation and collision detection to generate multiple traffic domains adapted to different traffic devices.

[0010] Furthermore, in step S10, the point cloud is denoised using a python function for removing outliers in the Open3D library.

[0011] Furthermore, in step S10, a laser mobile scanning device is used to perform laser point cloud scanning on the interior of the museum, using a point-by-point or segmented scanning mode.

[0012] Furthermore, in step S10, the Python registration function provided by the Open3D library is used to perform point cloud registration, and the iterative closest point algorithm is adopted to determine the relative pose between different point clouds with overlapping areas, including the rotation matrix R and the translation vector t, and the point set C corresponding to the point cloud is subjected to a rigid body transformation RC+t, so as to align multiple point clouds on the same floor and cross-floor point clouds to a unified spatial coordinate system.

[0013] Further, in step S20, the indoor space is a museum, and step S20 includes the following steps:

[0014] Step S20 includes the following steps:

[0015] Taking the set value as the partition, count the frequency of points collected in each vertical partition of the museum point cloud;

[0016] According to the number of floors k of the museum, a mixed Gaussian fit is performed. The number of Gaussian functions to be fitted is twice the number of floors. The derivative-free optimization method is used for optimization and solution to obtain the elevation of the ceiling and floor of each floor in the point cloud.

[0017] After estimating the floor elevation of each floor in the point cloud, the point cloud located in the corresponding elevation range is segmented out as the ground point cloud of each floor, providing reference data for ground height difference detection, barrier-free passage simulation and collision detection.

[0018] Furthermore, the set value is 0.3m.

[0019] Furthermore, step S30 also includes screening the passable areas for special groups of people, and the specific steps include:

[0020] S301, the side length of the three-dimensional voxel model is set according to the size requirements for special groups of people to pass through indoors, and the planar connected area in the three-dimensional voxel model is calculated;

[0021] S302, when the difference in elevation between adjacent ground points reaches or exceeds the set value of the side length of the three-dimensional voxel model, the two adjacent points are regarded as non-horizontally connected;

[0022] S303, the construction of the horizontally connected region is completed by the region growing algorithm under horizontal eight-connectivity.

[0023] Furthermore, the side length of the three-dimensional voxel model is set to 0.1 m.

[0024] Furthermore, in step S40, a wheelchair or baby stroller traffic simulation and collision detection are performed for wheelchairs or baby strollers used by special groups to ensure the passability of the traffic equipment in areas with a smaller range of activities.

[0025] Further, step S40 includes the following steps:

[0026] S401, collecting multiple representative wheelchair and baby stroller models, and generating a three-dimensional digital map of barrier-free passages corresponding to the multiple wheelchair and baby stroller models;

[0027] S402, calling the collision detection interface provided by Revit, inputting the wheelchair and baby carriage models one by one, and inputting the point cloud at the same time;

[0028] S403, traversing all voxel positions in each passable area, calculating when the horizontal center of the wheelchair or baby stroller model is placed at the voxel position, traversing the circumferential angle at intervals of a set angle, and checking whether the wheelchair or baby stroller model collides with the point cloud at each angle of the position;

[0029] S404. If the result returned by the Revit interface indicates that there is a collision, the voxel position is removed from the barrier-free voxels of the wheelchair or stroller model;

[0030] S405: After traversing all voxel regions and all angles, a barrier-free voxel model adapted to a single baby carriage or wheelchair model is generated.

[0031] The beneficial effects of the present invention are: it aims to solve the problem of difficult route planning for indoor barrier-free passages for special groups of people; in the absence of CAD drawings of buildings, it can utilize the rapidly developing LiDAR three-dimensional scanning technology and the relatively complete collision detection function in BIM software to construct a three-dimensional map of indoor barrier-free passages that can adapt to multiple types of barrier-free auxiliary passage equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a flow chart of the method for generating indoor three-dimensional barrier-free map based on LiDAR point cloud and BIM collision simulation of the present invention.

[0033] Figure 2 , Figure 3 This is a specific embodiment diagram of the method for generating indoor three-dimensional barrier-free maps based on LiDAR point cloud and BIM collision simulation of the present invention.

[0034] Figure 4 It is a schematic diagram of the horizontal eight-connectivity of the three-dimensional voxel model in the present invention. DETAILED DESCRIPTION

[0035] The present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0036] The following will clearly and completely describe the concept, specific structure and technical effects of the present invention in combination with the embodiments and drawings, so as to fully understand the purpose, characteristics and effects of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, other embodiments obtained by technicians in this field without creative work are all within the scope of protection of the present invention. In addition, all the connection / connection relationships involved in the patent do not refer to the direct connection of components, but refer to the formation of a better connection structure by adding or reducing connection accessories according to the specific implementation situation. The various technical features in the invention can be combined interchangeably without conflicting with each other.

[0037] Reference Figure 1 As shown, the present invention discloses a method for generating an indoor three-dimensional barrier-free map based on LiDAR point cloud and BIM collision simulation. In this embodiment, the method includes steps S10-S40, the contents of which are as follows:

[0038] S10, laser point cloud acquisition and preprocessing, performing laser point cloud acquisition on the indoor space and preprocessing the acquired multi-frame point clouds, the preprocessing process includes point cloud noise reduction processing and multi-frame point cloud registration;

[0039] In this embodiment, the indoor space is a museum.

[0040] In step S10, a laser mobile scanning device is used to perform laser point cloud scanning inside the museum, using a point-by-point or segmented scanning mode. The mobile scanning device allows the scanning personnel to move flexibly in the museum space, and can relatively freely adjust the scanning angle of the laser radar according to the surface of the scanned object, so that the scanning work of multiple floors of the museum can be completed in a relatively short time.

[0041] After scanning, the point cloud needs to be checked for quality, and there is usually some noise. To reduce the impact of point cloud noise on subsequent processing, this embodiment uses the Python function of removing outliers in the Open3D library to perform noise reduction on the point cloud.

[0042] In scanning operations of large indoor spaces such as museums, in order to reduce the positioning drift error caused by scanning and limited by the battery life of the equipment, in this embodiment, a point-by-point or segmented scanning mode is adopted, and then the multiple point cloud data collected are aligned.

[0043] Reference Figure 2 , Figure 3 As shown, for laser point cloud scanning, the present invention provides a specific embodiment, and its scanning route refers to Figure 2 and Figure 3 As shown in the figure, taking the G and 1 floors of the test site museum as an example, in order to reduce the positioning drift problem caused by the mobile scanning device during the scanning process, the scanning route is mostly planned as a closed loop to cover the hall and theme exhibition hall in the museum, such as Figure 3 As shown, other scan segments except segment 4. Secondly, in preparation for subsequent registration, it is necessary to ensure that there is enough overlap between adjacent scan segments, that is, it is necessary to adjust the scanning angle at the junctions such as the entrances and exits between the two halls, stairways, and corridors, and collect the area point cloud corresponding to the adjacent scan segments within the visible range. In addition, special attention should be paid to the stairs on the same floor during the scanning process.

[0044] After completing the laser point cloud scanning, in this embodiment, the Python registration function provided by the Open3D library is used to perform point cloud registration, and the iterative closest point algorithm is adopted to determine the relative pose between different point clouds in overlapping areas, including the rotation matrix R and the translation vector t, and the point set C corresponding to the point cloud is subjected to a rigid body transformation RC+t, and multiple point clouds on the same floor and cross-floor point clouds are registered to a unified spatial coordinate system.

[0045] S20, point cloud quantity statistics and elevation fitting, performing point cloud quantity statistics in the vertical direction on the pre-processed point cloud, performing elevation fitting of the floor and ceiling, and detecting the ground point cloud;

[0046] Step S20 includes the following steps:

[0047] The frequency of points collected by the museum point cloud in each vertical partition is counted with the set value as the partition; in this embodiment, the set value is 0.3m;

[0048] According to the number of floors k of the museum, a mixed Gaussian fit is performed. The number of Gaussian functions to be fitted is twice the number of floors. The derivative-free optimization method is used for optimization and solution to obtain the elevation of the ceiling and floor of each floor in the point cloud.

[0049] After estimating the floor elevation of each floor in the point cloud, the point cloud located in the corresponding elevation range is segmented out as the ground point cloud of each floor, providing reference data for ground height difference detection, barrier-free passage simulation and collision detection.

[0050] S30, generating a three-dimensional voxel model corresponding to the ground point cloud, extracting a general barrier-free passable area on the three-dimensional voxel model, and segmenting the passable voxel area at the location where the height difference suddenly changes;

[0051] Wheelchairs and strollers are generally not able to pass easily on floors with height differences such as steps, so step three also requires screening of accessible areas for special groups based on the height difference problem; the specific steps include:

[0052] S301, the side length of the 3D voxel model is set according to the size requirements for special groups of people to pass through indoors, and the planar connected area in the 3D voxel model is calculated; in this embodiment, the side length of the 3D voxel model is set to 0.1m;

[0053] S302, when the difference in elevation between adjacent ground points reaches or exceeds the set value of the side length of the three-dimensional voxel model, the two adjacent points are regarded as non-horizontally connected;

[0054] S303: The construction of the horizontally connected region is completed by the region growing algorithm under horizontal eight-connectivity. In this embodiment, the implementation of the algorithm can refer to the following function 1:

[0055]

[0056]

[0057] Combination Figure 4 As shown, this is a schematic diagram of the horizontal eight-connectivity of the three-dimensional voxel model.

[0058] The result of this step is a barrier-free ground voxel area that has passed the height difference screening. Barrier-free passage can be ensured between adjacent voxels, but barrier-free passage is not supported outside the edge of the voxel area.

[0059] S40, traffic simulation and collision detection, perform traffic simulation and collision detection to generate multiple traffic domains suitable for different traffic equipment; for wheelchairs or strollers used by special groups, perform traffic simulation and collision detection on wheelchairs or strollers to ensure the accessibility of traffic equipment in areas with a smaller range of activities.

[0060] In step S40, collision detection is performed on a variety of representative means of transportation for special persons, including wheelchairs and baby carriages, using BIM software to construct a barrier-free three-dimensional digital map suitable for different means of transportation.

[0061] Step S40 includes the following steps:

[0062] S401, collecting a plurality of representative wheelchair and baby stroller models, and generating a three-dimensional digital map of barrier-free passages corresponding to the plurality of wheelchair and baby stroller models; in this embodiment, 10 representative wheelchair and baby stroller models are collected;

[0063] S402, calling the collision detection interface provided by Revit, inputting the wheelchair and baby carriage models one by one, and inputting the point cloud at the same time;

[0064] S403, traverse all voxel positions in each passable area, calculate when the horizontal center of the wheelchair or baby stroller model is placed at the voxel position, traverse the circumferential angle at intervals of a set angle, and check whether the wheelchair or baby stroller model collides with the point cloud at each angle of the position; in this embodiment, the set angle is 15°;

[0065] S404. If the result returned by the Revit interface indicates that there is a collision, the voxel position is removed from the barrier-free voxels of the wheelchair or stroller model;

[0066] S405: After traversing all voxel regions and all angles, a barrier-free voxel model adapted to a single baby carriage or wheelchair model is generated.

[0067] Subsequent path planning and navigation functions can use A* algorithm and other algorithms to calculate the shortest path on the barrier-free voxel map.

[0068] The present invention provides a method for generating a three-dimensional digital map of indoor barrier-free passages based on LiDAR point cloud and BIM software collision detection, aiming to solve the problem of difficulty in planning routes for indoor barrier-free passages for special groups of people. In the absence of CAD drawings of buildings, the rapidly developing LiDAR three-dimensional scanning technology and the relatively complete collision detection function in BIM software can be used to construct a high-precision, fine-grained three-dimensional map of indoor barrier-free passages that can adapt to multiple types of barrier-free auxiliary passage equipment, which can meet the special indoor passage needs of wheelchairs, bicycles, etc. At the same time, the semi-automatic solution proposed by the present invention also reduces the demand for manpower in mapping work and reduces the cost of mapping.

[0069] The above is a specific description of the preferred implementation of the present invention, but the invention is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A method for generating indoor three-dimensional barrier-free maps based on LiDAR point cloud and BIM collision simulation, characterized in that: The method comprises the following steps: S10, laser point cloud acquisition and preprocessing, performing laser point cloud acquisition on the indoor space and preprocessing the acquired multi-frame point clouds, the preprocessing process includes point cloud noise reduction processing and multi-frame point cloud registration; S20, point cloud quantity statistics and elevation fitting, performing point cloud quantity statistics in the vertical direction on the pre-processed point cloud, performing elevation fitting of the floor and ceiling, and detecting the ground point cloud; S30, generating a three-dimensional voxel model corresponding to the ground point cloud, extracting a general barrier-free passable area on the three-dimensional voxel model, and segmenting the passable voxel area at the location where the height difference suddenly changes; S40, traffic simulation and collision detection, performing traffic simulation and collision detection to generate multiple traffic domains adapted to different traffic devices.

2. The method for generating indoor three-dimensional barrier-free maps based on LiDAR point cloud and BIM collision simulation according to claim 1, characterized in that: In step S10, the point cloud is denoised using a python function for removing outliers in the Open3D library.

3. The method for generating indoor three-dimensional barrier-free map based on LiDAR point cloud and BIM collision simulation according to claim 2, characterized in that: In step S10, a laser mobile scanning device is used to perform laser point cloud scanning on the interior of the museum, using a point-by-point or segmented scanning mode.

4. The method for generating indoor three-dimensional barrier-free maps based on LiDAR point cloud and BIM collision simulation according to claim 2, characterized in that: In step S10, the Python registration function provided by the Open3D library is used to perform point cloud registration. The iterative closest point algorithm is used to determine the relative poses between different point clouds in overlapping areas, including the rotation matrix R and the translation vector t. The point set C corresponding to the point cloud is subjected to a rigid body transformation RC+t, and multiple point clouds on the same floor and cross-floor point clouds are registered to a unified spatial coordinate system.

5. The method for generating indoor three-dimensional barrier-free map based on LiDAR point cloud and BIM collision simulation according to claim 1, characterized in that: In step S20, the indoor space is a museum, and step S20 includes the following steps: Taking the set value as the partition, count the frequency of points collected in each vertical partition of the museum point cloud; According to the number of floors k of the museum, a mixed Gaussian fit is performed. The number of Gaussian functions to be fitted is twice the number of floors. The derivative-free optimization method is used for optimization and solution to obtain the elevation of the ceiling and floor of each floor in the point cloud. After estimating the floor elevation of each floor in the point cloud, the point cloud located in the corresponding elevation range is segmented out as the ground point cloud of each floor, providing reference data for ground height difference detection, barrier-free passage simulation and collision detection.

6. The method for generating indoor three-dimensional barrier-free maps based on LiDAR point cloud and BIM collision simulation according to claim 5, characterized in that: The set value is 0.3m.

7. The method for generating indoor three-dimensional barrier-free map based on LiDAR point cloud and BIM collision simulation according to claim 1, characterized in that: Step S30 also includes screening the passable areas for special groups of people, and the specific steps include: S301, the side length of the three-dimensional voxel model is set according to the size requirements for special groups of people to pass through indoors, and the planar connected area in the three-dimensional voxel model is calculated; S302, when the difference in elevation between adjacent ground points reaches or exceeds the set value of the side length of the three-dimensional voxel model, the two adjacent points are regarded as non-horizontally connected; S303, the construction of the horizontally connected region is completed by the region growing algorithm under horizontal eight-connectivity.

8. The method for generating indoor three-dimensional barrier-free maps based on LiDAR point cloud and BIM collision simulation according to claim 7, characterized in that: The side length of the 3D voxel model is set to 0.1m.

9. The method for generating indoor three-dimensional barrier-free maps based on LiDAR point cloud and BIM collision simulation according to claim 7, characterized in that: In step S40, for wheelchairs or baby strollers used by special groups, passage simulation and collision detection of the wheelchairs or baby strollers are performed to ensure the passability of the passage equipment in areas with a smaller range of activities.

10. The method for generating indoor three-dimensional barrier-free maps based on LiDAR point cloud and BIM collision simulation according to claim 9, characterized in that: Step S40 includes the following steps: S401, collecting multiple representative wheelchair and baby stroller models, and generating a three-dimensional digital map of barrier-free passages corresponding to the multiple wheelchair and baby stroller models; S402, calling the collision detection interface provided by Revit, inputting the wheelchair and baby carriage models one by one, and inputting the point cloud at the same time; S403, traversing all voxel positions in each passable area, calculating when the horizontal center of the wheelchair or baby stroller model is placed at the voxel position, traversing the circumferential angle at intervals of a set angle, and checking whether the wheelchair or baby stroller model collides with the point cloud at each angle of the position; S404. If the result returned by the Revit interface indicates that there is a collision, the voxel position is removed from the barrier-free voxels of the wheelchair or stroller model; S405: After traversing all voxel regions and all angles, a barrier-free voxel model adapted to a single baby carriage or wheelchair model is generated.

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