Three-dimensional environment analysis method and apparatus, computer storage medium, and wireless sensor system

Abstract

This invention relates to a three-dimensional environment analysis method. The method includes: receiving raw point cloud data of a work environment; processing a map constructed based on the raw point cloud data to separate the ground, walls, and obstacles in the work environment; pairing the ground and walls according to their proximity to form one or more adjacent ground-wall pairs; and performing ray tracing analysis on the one or more adjacent ground-wall pairs to obtain the direct-view area and non-direct-view area in the work environment. This invention also relates to a three-dimensional environment analysis device, a computer storage medium, and a wireless sensor system.

Description

Technical Field

[0001] This invention relates to a three-dimensional environment analysis scheme, and more specifically, to a three-dimensional environment analysis method and apparatus, a computer storage medium, and a wireless sensor system. Background Technology

[0002] When setting up a wireless signal propagation system in complex environments, non-line-of-sight (NLOS) situations can occur between the anchor point and connected devices (e.g., localized tags connected to an anchor in a UWB system, mobile computers connected to a WiFi anchor, etc.) during wireless connection. This often leads to degraded system performance or even malfunction. For example, in a UWB (Ultra Wide Band) system, when there is an NLOS situation between the localized tag and the wall-mounted UWB anchor, the system cannot accurately track the location of the UWB tag.

[0003] Not only in UWB wireless sensor systems, but also in other systems that require a line-of-sight (LOS) between the sensor or anchor and the tracked device for optimal functioning, NLOS is avoided as much as possible. For example, in situations where multiple surveillance cameras are installed to maximize environmental coverage, or in WiFi systems, NLOS can degrade system performance.

[0004] When initially setting up a wireless sensor system, extensive manual measurements and careful selection of anchor (or sensor) mounting points are required to minimize NLOS (Normally Independent Operations) during system operation. In complex, heterogeneous environments, predicting the optimal mounting point is extremely difficult.

[0005] Therefore, an improved three-dimensional environment analysis scheme is desired to help users set up wireless sensor systems more conveniently. Summary of the Invention

[0006] According to one aspect of the present invention, a three-dimensional environment analysis method is provided, the method comprising: receiving raw point cloud data of a work environment; processing a map constructed based on the raw point cloud data to separate ground, walls, and obstacles in the work environment; pairing the ground and the walls according to the proximity between the separated ground and the walls to form one or more adjacent ground-wall pairing groups; and performing ray tracing analysis on the one or more adjacent ground-wall pairing groups to obtain the direct-view area and the non-direct-view area in the work environment.

[0007] Optionally, in the above method, receiving raw point cloud data of the working environment includes receiving raw point cloud data generated by a laser detection and ranging system or a depth camera.

[0008] Optionally, in the above method, processing the map constructed based on the original point cloud data includes: using SLAM technology to process the original point cloud data to construct a 3D map of the working environment; preprocessing the map to remove noise and / or outliers; and segmenting the preprocessed map to extract the ground, walls, and obstacles in the working environment.

[0009] Optionally, in the above method, performing ray tracing analysis on the one or more adjacent ground-wall pairs includes: for each of the n wall points on the wall in each pair, tracing m rays from each wall point to m points on the ground in each pair; if the first ray of the m rays does not encounter any obstacles or walls during its transmission, then the first ray is considered to be line-of-sight transmission; and if the second ray of the m rays encounters obstacles or walls during its transmission, then the second ray is considered to be non-line-of-sight transmission.

[0010] Optionally, in the above method, performing ray tracing analysis on the one or more adjacent ground-wall pairs further includes: for each of the n wall points, obtaining the number and length of rays that are respectively identified as line-of-sight transmission and non-line-of-sight transmission.

[0011] Optionally, the above method may further include: visualizing the ground, walls, and obstacles in a graphical user interface for users to browse and mark.

[0012] Optionally, the above method may further include: graphically displaying the direct viewing area and the non-direct viewing area for a three-dimensional point or three-dimensional region in a work environment selected by the user.

[0013] Optionally, the above method may further include: calculating and outputting an ideal anchor point position on the wall based on the ray tracing analysis, wherein the ideal anchor point position minimizes non-direct-view situations within a pre-selected area in the entire working environment.

[0014] Optionally, the above method may further include: based on the ray tracing analysis, providing a statistical analysis of the relationship between the area ratio of the non-direct-view area to the direct-view area and the number of anchor points deployed on the wall and the installation position of the anchor points.

[0015] Optionally, in the above method, based on the ray tracing analysis and combined with user-defined constraints, the ideal anchor point position on the wall is calculated and output.

[0016] According to another aspect of the present invention, a three-dimensional environment analysis device is provided, the device comprising: a receiving module for receiving raw point cloud data of a working environment; a processing module for processing a map constructed based on the raw point cloud data to separate ground, walls, and obstacles in the working environment; a pairing module for pairing the ground and the wall according to the proximity between the separated ground and the wall to form one or more adjacent ground-wall pairing groups; and an analysis module for performing ray tracing analysis on the one or more adjacent ground-wall pairing groups to obtain the direct-view area and the non-direct-view area in the working environment.

[0017] Optionally, in the above-described device, the receiving module is configured to receive raw point cloud data generated by a laser detection and ranging system or a depth camera.

[0018] Optionally, in the above-described device, the processing module includes: a construction module for using SLAM technology to process raw point cloud data to construct a 3D map of the working environment; a preprocessing module for preprocessing the raw point cloud data to remove noise and / or outliers; and a point cloud segmentation module for segmenting the preprocessed point cloud data to extract the ground, walls, and obstacles in the working environment.

[0019] Optionally, in the above device, the analysis module is configured to, for each of the n wall points on the wall in each pairing group, track m light rays between each wall point and m points on the ground in each pairing group; if the first light ray among the m light rays does not encounter any obstacles or walls during its transmission, the first light ray is identified as direct line-of-sight transmission; and if the second light ray among the m light rays encounters obstacles or walls during its transmission, the second light ray is identified as non-direct line-of-sight transmission.

[0020] Optionally, in the above-described device, the analysis module is further configured to obtain, for each of the n wall points, the number and length of light rays respectively identified as direct-line transmission and non-direct-line transmission.

[0021] Optionally, the above-mentioned device may further include: a first graphics display module for visualizing the ground, walls and obstacles in a graphical user interface for users to browse and mark.

[0022] Optionally, the above-mentioned device may further include: a second image display module, used to graphically display the direct viewing area and the non-direct viewing area for a three-dimensional point or three-dimensional area in a working environment selected by the user.

[0023] Optionally, the above-mentioned device may further include: a calculation module for calculating and outputting an ideal anchor point position on the wall based on the ray tracing analysis, wherein the ideal anchor point position minimizes non-direct-view situations within a pre-selected area in the entire working environment.

[0024] Optionally, the above-mentioned device may further include: a statistical analysis module, used to provide statistical analysis of the relationship between the area ratio of the non-direct viewing area and the direct viewing area and the number and installation position of the anchor points deployed on the wall, based on the ray tracing analysis.

[0025] Optionally, in the above-described device, the calculation module is configured to calculate and output the ideal anchor point position on the wall based on the ray tracing analysis and in conjunction with user-defined constraints.

[0026] According to another aspect of the present invention, a computer storage medium is provided, the medium including instructions that, when executed, perform the three-dimensional environment analysis method as described above.

[0027] According to another aspect of the present invention, a wireless sensor system is provided, which includes the three-dimensional environment analysis device as described above.

[0028] In summary, the three-dimensional environment analysis solution of the present invention can help users set up wireless sensor systems faster and more conveniently. Attached Figure Description

[0029] The above and other objects and advantages of the present invention will become more fully clear from the following detailed description taken in conjunction with the accompanying drawings, wherein the same or similar elements are indicated by the same reference numerals.

[0030] Figure 1 A flowchart illustrating a three-dimensional environment analysis method according to an embodiment of the present invention is shown; and

[0031] Figure 2 A schematic diagram of a three-dimensional environmental analysis device according to an embodiment of the present invention is shown. Detailed Implementation

[0032] In the following, a three-dimensional environment analysis scheme according to various exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings.

[0033] Figure 1 A flowchart illustrating a three-dimensional environment analysis method 1000 according to an embodiment of the present invention is shown. Figure 1 As shown, method 1000 includes the following steps:

[0034] In step S110, the raw point cloud data of the working environment is received;

[0035] In step S120, the map constructed based on the original point cloud data is processed to separate the ground, walls and obstacles in the working environment;

[0036] In step S130, the ground and the wall are paired according to the proximity between the separated ground and wall surfaces to form one or more adjacent ground-wall pairing groups; and

[0037] In step S140, ray tracing analysis is performed on the one or more adjacent ground-wall pairs to obtain the direct-view area and the non-direct-view area in the working environment.

[0038] In the context of this invention, the term "operating environment" means any environment in which a wireless sensor system or a wireless signal propagation system is installed or is prepared to be installed.

[0039] The term "point cloud" refers to a collection of points obtained after acquiring the spatial coordinates of each sampled point on the surface of an object. In one embodiment, "point cloud data" may include information such as two-dimensional coordinates (XY) or three-dimensional coordinates (XYZ), laser reflection intensity, and color information (RGB). "Raw point cloud data" refers to point cloud data without preprocessing. In one embodiment, raw point cloud data may be generated by a laser detection and ranging system (LiDAR) or a depth camera (such as an RGB-D camera).

[0040] In the context of this invention, the term "obstacle" is used in contrast to "wall" and "ground" and refers to any object that is not part of a wall or ground in the working environment where the wireless sensor system is set up.

[0041] The term "line-of-sight" is also known as line-of-sight (LOS), and the term "non-line-of-sight" is also known as non-line-of-sight (NLOS). In terms of name, they refer to line-of-sight and non-line-of-sight transmission of wireless signals, respectively. In practical mobile communication network planning, most environments can be divided into LOS and NLOS.

[0042] Under Line-of-Sight (LOS) conditions, wireless signals propagate 'in a straight line' between the transmitter and receiver without obstruction. This requires that there are no objects obstructing the radio waves within the First Fresnel zone. If this condition is not met, the signal strength will significantly decrease. The size of the Fresnel zone depends on the frequency of the radio waves and the distance between the transmitter and receiver. However, when there are obstacles, the wireless signal can only reach the receiver through reflection, scattering, and diffraction. This is called Non-Line-of-Sight (NLOS), or Non-Line-of-Sight transmission. In this case, the wireless signal is received through multiple paths, and multipath effects can lead to a series of problems such as asynchronous timing, signal attenuation, polarization changes, and link instability.

[0043] In the context of this invention, the term "line-of-sight zone" refers to a specific ground area in the working environment where, when a moving object moves within this area, the wireless signal propagation between a sensor or anchor mounted on a wall (e.g., an anchor in a UWB system) and the moving object (i.e., the tracked object) is line-of-sight transmission. Similarly, the term "non-line-of-sight zone" has the opposite meaning to "line-of-sight zone," referring to the wireless signal propagation between a sensor or anchor mounted on a wall (e.g., an anchor in a UWB system) and the tracked object when the tracked object moves within a non-line-of-sight zone, which is non-line-of-sight transmission.

[0044] By using the three-dimensional environment analysis method described above, and performing ray tracing analysis on one or more adjacent ground-wall pairs, the direct-view and non-direct-view areas in the working environment can be obtained, indicating the ideal anchor point location that minimizes the non-direct-view area, thereby helping users to set up wireless sensor systems faster and more conveniently.

[0045] In one embodiment, step S110 may include: receiving raw point cloud data generated by a laser detection and ranging system (LiDAR) or a depth camera (e.g., an RGB-D camera), and loading the raw point cloud data into a database. In one embodiment, the raw point cloud data can be generated using LiDAR-based SLAM (Simultaneous Localization and Mapping) technology. The emergence and widespread use of LiDAR has made measurements faster, more accurate, and richer in information. The object information collected by LiDAR presents a series of dispersed points with accurate angle and distance information, called a point cloud. Typically, a LiDAR SLAM system calculates the changes in distance and attitude of the relative motion of the LiDAR by matching and comparing two point clouds at different times, thus completing the localization of the robot itself. LiDAR ranging is relatively accurate, its error model is simple, it operates stably in environments other than direct sunlight, and point cloud processing is relatively easy. At the same time, the point cloud information itself contains direct geometric relationships, making the robot's path planning and navigation more intuitive.

[0046] In one embodiment, step S120 may include: using SLAM (Simultaneous Localization and Mapping) technology to process raw point cloud data to construct a 3D map of the work environment; preprocessing the map to remove noise and / or outliers; and segmenting the preprocessed map to extract the ground, walls, and obstacles in the work environment. In one embodiment, the 3D map of the work environment is preprocessed using functions provided by a Point Cloud Library (PCL) to remove noise and / or outliers. In another embodiment, functions such as RANSAC and Euclidean clustering in the PCL are used to segment the preprocessed map to extract the ground, walls, and obstacles in the work environment.

[0047] RANSAC stands for Random Consistent Sampling, which mainly solves the problem of outliers in the sample and can handle up to 50% of outlier cases. The basic idea of ​​RANSAC is to achieve the goal by repeatedly selecting a random subset of the data. The selected subset is assumed to be inliers and is verified by the following methods: (1) There is a model that fits the assumed inliers, that is, all unknown parameters can be calculated from the assumed inliers; (2) Use the model obtained in step 1 to test all other data. If a point fits the estimated model, it is considered to be an inlier; (3) If enough points are classified as assumed inliers, then the estimated model is reasonable enough; (4) Re-estimate the model using all the assumed inliers, since it has only been estimated by the initial assumed inliers; (5) Evaluate the model by estimating the error rate of the inliers and the model.

[0048] This process is repeated a fixed number of times, and each resulting model is either discarded because it has too few inliers or selected because it is better than existing models. The RANSAC algorithm is well-suited for detecting objects with specific shapes from cluttered point clouds.

[0049] Euclidean clustering is a clustering algorithm based on the Euclidean distance metric. For Euclidean clustering, the distance criterion is Euclidean distance. For a point P in space, the KD-Tree nearest neighbor search algorithm finds k points closest to P. Points whose distance to P is less than a set threshold are clustered into a set Q. If the number of elements in Q no longer increases, the entire clustering process ends; otherwise, points other than P are selected from set Q, and the above process is repeated until the number of elements in Q no longer increases.

[0050] In step S130, the ground and wall surfaces are paired according to their proximity to each other to form one or more adjacent ground-wall pairing groups. For example, each segmented ground and wall surface is paired based on the proximity between reference planes and points on the ground and wall surfaces. In other words, when the proximity between the ground and wall surface is greater than a threshold S, the ground surface and wall surface are paired.

[0051] In one embodiment, step S140 may include: for each of the n wall points on the wall in each pairing group, tracing m light rays from each wall point to m points on the ground in each pairing group; if a first light ray among the m light rays does not encounter any obstacles or walls during its transmission, then the first light ray is identified as direct line-of-sight transmission; and if a second light ray among the m light rays encounters an obstacle or wall during its transmission, then the second light ray is identified as non-direct line-of-sight transmission. In one embodiment, step S140 may further include: for each of the n wall points, obtaining the number and length of light rays respectively identified as direct line-of-sight transmission and non-direct line-of-sight transmission.

[0052] although Figure 1 As not shown in the diagram, in one embodiment, the 3D environment analysis method 1000 may further include: visualizing the ground, walls, and obstacles in a graphical user interface for the user to browse and mark. The user may use measurement tools similar to CAD programs to determine the distances and angles between the geometric features of the reconstructed environment, mark 3D locations and areas on a virtual layout for subsequent NLOS analysis, and select anchor points / sensor installations.

[0053] In one embodiment, the three-dimensional environment analysis method 1000 may further include: graphically displaying the direct viewing area and the non-direct viewing area for three-dimensional points or three-dimensional regions in a work environment selected by the user.

[0054] In one embodiment, the 3D environment analysis method 1000 may further include: calculating and outputting ideal anchor point positions on the wall based on the ray tracing analysis, the ideal anchor point positions minimizing non-direct-view situations within a pre-selected area of ​​the entire working environment. This provides visual and textual output (e.g., 3D coordinates in a text file) in the virtual environment layout.

[0055] In one embodiment, the three-dimensional environment analysis method 1000 may further include: based on the ray tracing analysis (e.g., at least based on the number and length of rays in direct and non-direct transmission), providing a statistical analysis of the relationship between the area ratio of the non-direct-view area to the direct-view area and the number of anchor points deployed on the wall, the installation location of the anchor points, etc.

[0056] Users can also specify multiple constraints, such as the number of anchors / sensors to be deployed, the minimum / maximum allowable anchor / sensor installation height, etc. One or more of the above embodiments can take these constraints into account in the output and display. In one embodiment, based on the ray tracing analysis and combined with user-defined constraints, the ideal anchor point position on the wall is calculated and output.

[0057] The graphical user interface also allows users to specify the final anchor / sensor locations based on the analysis described above. After specifying the final location, step-by-step visual guidance is provided to the user to install individual anchors within the 3D layout. Virtual suggestions and distances to the next visual landmark (such as edges and the ground) can also be displayed. For example, the user might be advised to "install anchor 1 on wall 1, which is 5 meters above the ground and 0.8 meters from the left edge." Furthermore, all visualizations and analysis results can be exported as images or text for sharing and documentation.

[0058] refer to Figure 2 , Figure 2 A schematic diagram of the structure of a three-dimensional environmental analysis device 2000 according to an embodiment of the present invention is shown. Figure 2 As shown, the 3D environment analysis device 2000 includes a receiving module 210, a processing module 220, a pairing module 230, and an analysis module 240. The receiving module 210 receives raw point cloud data of the working environment; the processing module 220 processes the map constructed based on the raw point cloud data to separate the ground, walls, and obstacles in the working environment; the pairing module 230 pairs the ground and walls according to their proximity to form one or more adjacent ground-wall pairing groups; and the analysis module 240 performs ray tracing analysis on the one or more adjacent ground-wall pairing groups to obtain the direct-view area and non-direct-view area in the working environment.

[0059] In the context of this invention, the term "operating environment" means any environment in which a wireless sensor system or a wireless signal propagation system is installed or is prepared to be installed.

[0060] The term "point cloud" refers to a collection of points obtained after acquiring the spatial coordinates of each sampled point on the surface of an object. In one embodiment, "point cloud data" may include information such as two-dimensional coordinates (XY) or three-dimensional coordinates (XYZ), laser reflection intensity, and color information (RGB). "Raw point cloud data" refers to point cloud data without preprocessing. In one embodiment, raw point cloud data may be generated by a laser detection and ranging system (LiDAR) or a depth camera (such as an RGB-D camera).

[0061] In the context of this invention, the term "obstacle" is used in contrast to "wall" and "ground" and refers to any object that is not part of a wall or ground in the working environment where the wireless sensor system is set up.

[0062] The term "line-of-sight" is also known as line-of-sight (LOS), and the term "non-line-of-sight" is also known as non-line-of-sight (NLOS). In terms of name, they refer to line-of-sight and non-line-of-sight transmission of wireless signals, respectively. In practical mobile communication network planning, most environments can be divided into LOS and NLOS.

[0063] Under Line-of-Sight (LOS) conditions, wireless signals propagate 'in a straight line' between the transmitter and receiver without obstruction. This requires that there are no objects obstructing the radio waves within the First Fresnel zone. If this condition is not met, the signal strength will significantly decrease. The size of the Fresnel zone depends on the frequency of the radio waves and the distance between the transmitter and receiver. However, when there are obstacles, the wireless signal can only reach the receiver through reflection, scattering, and diffraction. This is called Non-Line-of-Sight (NLOS), or Non-Line-of-Sight transmission. In this case, the wireless signal is received through multiple paths, and multipath effects can lead to a series of problems such as asynchronous timing, signal attenuation, polarization changes, and link instability.

[0064] The term "line-of-sight zone" refers to a specific ground area in the work environment where, when a moving object moves within this area, the wireless signal propagation between a wall-mounted sensor or anchor (e.g., an anchor in a UWB system) and the moving object (i.e., the tracked object) is line-of-sight transmission. Similarly, the term "non-line-of-sight zone" is the opposite of "line-of-sight zone," referring to the wireless signal propagation between a wall-mounted sensor or anchor (e.g., an anchor in a UWB system) and the tracked object when the object moves within the non-line-of-sight zone.

[0065] By using the aforementioned 3D environment analysis device 2000, ray tracing analysis is performed on one or more adjacent ground-wall pairs via the analysis module 240. This allows the acquisition of the direct-view and non-direct-view areas in the working environment, indicating the ideal anchor point location that minimizes the non-direct-view area. This helps users set up wireless sensor systems more quickly and conveniently.

[0066] In one embodiment, the receiving module 210 is configured to receive raw point cloud data generated by a LiDAR (LiDAR) system or a depth camera (e.g., an RGB-D camera) and load the raw point cloud data into a database. In one embodiment, the raw point cloud data can be generated using LiDAR-based SLAM (Simultaneous Localization and Mapping) technology. The emergence and widespread adoption of LiDAR has made measurements faster, more accurate, and richer in information. The object information collected by LiDAR presents a series of dispersed points with accurate angle and distance information, known as a point cloud. Typically, a LiDAR SLAM system calculates the changes in distance and attitude of the relative motion of the LiDAR by matching and comparing two point clouds at different times, thus completing the localization of the robot itself. LiDAR ranging is relatively accurate, its error model is simple, it operates stably in environments other than direct sunlight, and point cloud processing is relatively easy. Furthermore, the point cloud information itself contains direct geometric relationships, making robot path planning and navigation more intuitive.

[0067] In one embodiment, the processing module 220 includes a construction module, a preprocessing module, and a point cloud segmentation module. The construction module is used to process the raw point cloud data using SLAM (Simultaneous Localization and Mapping) technology to construct a 3D map of the working environment. The preprocessing module is used to preprocess the map to remove noise and / or outliers. The point cloud segmentation module is used to segment the preprocessed map to extract the ground, walls, and obstacles in the working environment.

[0068] In one embodiment, the preprocessing module preprocesses the 3D map of the work environment using the functions provided by the Point Cloud Library (PCL) to remove noise and / or outliers. In another embodiment, the point cloud segmentation module uses functions such as RANSAC and Euclidean clustering from the point cloud library to segment the preprocessed map in order to extract the ground, walls, and obstacles in the work environment.

[0069] RANSAC stands for Random Consistent Sampling, which mainly solves the problem of outliers in the sample and can handle up to 50% of outlier cases. The basic idea of ​​RANSAC is to achieve the goal by repeatedly selecting a random subset of the data. The selected subset is assumed to be inliers and is verified by the following methods: (1) There is a model that fits the assumed inliers, that is, all unknown parameters can be calculated from the assumed inliers; (2) Use the model obtained in step 1 to test all other data. If a point fits the estimated model, it is considered to be an inlier; (3) If enough points are classified as assumed inliers, then the estimated model is reasonable enough; (4) Re-estimate the model using all the assumed inliers, since it has only been estimated by the initial assumed inliers; (5) Evaluate the model by estimating the error rate of the inliers and the model.

[0070] This process is repeated a fixed number of times, and each resulting model is either discarded because it has too few inliers or selected because it is better than existing models. The RANSAC algorithm is well-suited for detecting objects with specific shapes from cluttered point clouds.

[0071] Euclidean clustering is a clustering algorithm based on the Euclidean distance metric. For Euclidean clustering, the distance criterion is Euclidean distance. For a point P in space, the KD-Tree nearest neighbor search algorithm finds k points closest to P. Points whose distance to P is less than a set threshold are clustered into a set Q. If the number of elements in Q no longer increases, the entire clustering process ends; otherwise, points other than P are selected from set Q, and the above process is repeated until the number of elements in Q no longer increases.

[0072] The pairing module 230 is used to pair the ground and the wall according to the proximity between the separated ground and wall surfaces to form one or more adjacent ground-wall pairing groups. For example, the configuration module 230 pairs each segmented ground and wall surface according to the proximity between the ground and the wall surface's reference plane and points. In other words, when the proximity between the ground and the wall surface is greater than a threshold S, the configuration module 230 pairs the ground surface with the wall surface.

[0073] In one embodiment, the analysis module 240 is configured to, for each of the n wall points on the wall in each pairing group, track m light rays between each wall point and m points on the ground in each pairing group; if a first light ray among the m light rays does not encounter any obstacles or walls during its transmission, the first light ray is identified as direct line-of-sight transmission; and if a second light ray among the m light rays encounters obstacles or walls during its transmission, the second light ray is identified as non-direct line-of-sight transmission. In one embodiment, the analysis module 240 is further configured to, for each of the n wall points, obtain the number and length of light rays identified as direct line-of-sight transmission and non-direct line-of-sight transmission, respectively.

[0074] although Figure 2 As not shown in the diagram, in one embodiment, the 3D environment analysis device 2000 may further include a first graphics display module that visualizes the ground, walls, and obstacles in a graphical user interface for the user to browse and mark. The user can use measurement tools similar to those in CAD programs to determine distances and angles between geometric features of the reconstructed environment, mark 3D locations and areas on a virtual layout for subsequent NLOS analysis, and select anchor points / sensor installations.

[0075] In one embodiment, the three-dimensional environment analysis device 2000 may further include a second graphics display module, which graphically displays the direct viewing area and the non-direct viewing area for three-dimensional points or three-dimensional regions in the working environment selected by the user.

[0076] In one embodiment, the 3D environment analysis device 2000 may further include a calculation module that, based on the ray tracing analysis, calculates and outputs ideal anchor point positions on the wall surface, which minimize non-direct-view elements within a pre-selected area of ​​the entire working environment. This provides visual and textual output (e.g., 3D coordinates in a text file) in the virtual environment layout.

[0077] In one embodiment, the three-dimensional environment analysis device 2000 may further include a statistical analysis module, which provides a statistical analysis of the relationship between the area ratio of the non-direct-view area to the direct-view area and the number of anchor points deployed on the wall, the installation position of the anchor points, etc., based on the ray tracing analysis (e.g., at least based on the number and length of rays in direct-view and non-direct-view transmission).

[0078] Users can also specify multiple constraints, such as the number of anchors / sensors to be deployed, the minimum / maximum allowable anchor / sensor installation height, etc. For example, a second graphics display module, a calculation module, and / or a statistical analysis module can take these constraints into account in the output and display. In one embodiment, the calculation module calculates and outputs the ideal anchor point position on the wall based on the ray tracing analysis and in conjunction with the user-defined constraints.

[0079] In some embodiments, the 3D environment analysis device 2000 may also allow the user to specify the final anchor / sensor positions based on the analysis described above in a graphical user interface. After specifying the final positions, step-by-step visual guidance is provided to the user to install individual anchors in the 3D layout. Virtual suggestions and distances to the next visual landmark (e.g., edges and ground) may also be displayed. For example, the user may be advised to “install anchor 1 on wall 1, which is 5 meters above the ground and 0.8 meters from the left edge.” Furthermore, all visualizations and analysis results may be exported in image or text form for sharing and documentation.

[0080] Those skilled in the art will readily understand that the three-dimensional environment analysis method provided in one or more embodiments of the present invention can be implemented by a computer program. For example, when a computer storage medium (e.g., a USB flash drive) containing the computer program is connected to a computer, running the computer program will execute the three-dimensional environment analysis method of the embodiments of the present invention.

[0081] In summary, the 3D environment analysis scheme of this invention can automatically reconstruct the environment and segment walls / obstacles and the ground using 2D or 3D point clouds / meshes, and automatically generate 2D and 3D layouts through reconstruction and segmentation. Layout dimensions (e.g., metric distances) can be automatically and manually measured and the layout geometry visualized using tools and a graphical user interface (GUI). Automatic ray tracing analysis is performed between all wall / obstacle and ground points to qualitatively and quantitatively analyze NLOS situations based on different anchor / sensor installation locations and constraints. User-defined visual and statistical evaluations of LOS / NLOS situations can also be performed based on ray tracing data using tools and the GUI. Constraints (e.g., desired anchor / sensor locations, anchor counts, etc.) can also be set.

[0082] In one or more embodiments, the three-dimensional environmental analysis scheme of the present invention enables the calculation and visual display of optimal anchor / sensor installation locations in wireless sensor systems (e.g., UWB positioning systems including anchors and tags). A step-by-step visual guidance system can be used for anchor / sensor installation, displaying the anchor / sensor's location and installation prompts (e.g., metric distances from the ground, walls, etc.). When new input data (point cloud or layout) becomes available, the tool can be automatically rerun to generate an updated layout, display updated LOS / NLOS status, and provide suggestions for device readjustment. All these functions significantly reduce the time spent on manual measurement, setup, and readjustment due to environmental changes or poor system performance.

[0083] The above examples primarily illustrate the three-dimensional environmental analysis scheme of the present invention. Although only some embodiments of the present invention have been described, those skilled in the art should understand that the present invention can be implemented in many other forms without departing from its spirit and scope. Therefore, the examples and embodiments shown are to be considered illustrative rather than restrictive, and the present invention may cover various modifications and substitutions without departing from the spirit and scope of the invention as defined by the appended claims.

Claims

1. A three-dimensional environment analysis method, characterized in that, The method includes: Receive raw point cloud data of the working environment; The map constructed based on the original point cloud data is processed to separate the ground, walls and obstacles in the working environment; Based on the proximity between the separated ground and wall surfaces, the ground surfaces and the wall surfaces are paired to form one or more adjacent ground-wall pairing groups; and Ray tracing analysis is performed on the one or more adjacent ground-wall pairs to obtain the direct-view and indirect-view areas in the work environment. The method further includes: Based on the ray tracing analysis, the ideal anchor point position on the wall is calculated and output. The ideal anchor point position can minimize the non-direct view within the pre-selected area in the entire working environment.

2. The method as described in claim 1, wherein, The raw point cloud data received from the working environment includes: It receives raw point cloud data generated by laser detection and ranging systems or depth cameras.

3. The method as described in claim 1, wherein, Processing the map constructed based on the original point cloud data includes: SLAM technology is used to process raw point cloud data to construct a 3D map of the work environment; The map is preprocessed to remove noise and / or outliers; and The preprocessed map is segmented to extract the ground, walls, and obstacles in the work environment.

4. The method of claim 1, wherein, Performing ray tracing analysis on the one or more adjacent ground-wall pairs includes: For each of the n wall points on the wall in each pairing group, trace the m light rays from each wall point to the m points on the ground in each pairing group; If the first ray of the m rays does not encounter any obstacles or walls during its transmission, then the first ray is considered to be in line-of-sight transmission; and If the second ray of the m rays encounters an obstacle or wall during its transmission, the second ray is considered to be non-line-of-sight transmission.

5. The method of claim 4, wherein, Performing ray tracing analysis on the one or more adjacent ground-wall pairs further includes: For each of the n wall points, obtain the number and length of light rays that are respectively identified as direct line-of-sight transmission and non-direct line-of-sight transmission.

6. The method of claim 1, further comprising: The ground, walls, and obstacles are visualized in a graphical user interface for users to browse and mark.

7. The method of claim 1, further comprising: For a three-dimensional point or three-dimensional region in the work environment selected by the user, the direct viewing area and the non-direct viewing area are graphically displayed.

8. The method of claim 1, further comprising: Based on the aforementioned ray tracing analysis, a statistical analysis is provided on the relationship between the area ratio of the non-direct-view area to the direct-view area and the number of anchor points deployed on the wall and the installation position of the anchor points.

9. The method of claim 1, wherein, Based on the ray tracing analysis and combined with the user-defined constraints, the ideal anchor point position on the wall is calculated and output.

10. A three-dimensional environmental analysis device, characterized in that, The device includes: The receiving module is used to receive raw point cloud data from the working environment; The processing module is used to process the map constructed based on the original point cloud data in order to separate the ground, walls and obstacles in the working environment; A pairing module is used to pair the ground surface and the wall surface according to the proximity between the separated ground and wall surfaces to form one or more adjacent ground-wall pairing groups; and The analysis module is used to perform ray tracing analysis on the one or more adjacent ground-wall pairs to obtain the direct-view and indirect-view areas in the working environment. The device also includes: The calculation module is used to calculate and output the ideal anchor point position on the wall based on the ray tracing analysis. The ideal anchor point position can minimize the non-direct view in the pre-selected area of ​​the entire working environment.

11. The device as claimed in claim 10, wherein, The receiving module is configured to receive raw point cloud data generated by a laser detection and ranging system or a depth camera.

12. The device as claimed in claim 10, wherein, The processing module includes: A building module is used to process raw point cloud data using SLAM technology to build a 3D map of the working environment; A preprocessing module is used to preprocess the map to remove noise and / or outliers; and The point cloud segmentation module is used to segment the preprocessed map in order to extract the ground, walls and obstacles in the working environment.

13. The device as claimed in claim 10, wherein, The analysis module is configured to track m light rays from each of the n wall points on the wall in each pairing group to m points on the ground in each pairing group for each wall point; if the first light ray of the m light rays does not encounter any obstacles or walls during its transmission, the first light ray is identified as direct line-of-sight transmission; and if the second light ray of the m light rays encounters obstacles or walls during its transmission, the second light ray is identified as non-direct line-of-sight transmission.

14. The device as claimed in claim 13, wherein, The analysis module is also configured to obtain the number and length of light rays that are respectively identified as direct line-of-sight transmission and non-direct line-of-sight transmission for each of the n wall points.

15. The apparatus of claim 10, further comprising: The first graphics display module is used to visualize the ground, walls, and obstacles in a graphical user interface for users to browse and mark.

16. The apparatus of claim 10, further comprising: The second image display module is used to graphically display the direct viewing area and the non-direct viewing area for a three-dimensional point or three-dimensional region in the working environment selected by the user.

17. The apparatus of claim 10, further comprising: The statistical analysis module is used to provide statistical analysis of the relationship between the area ratio of the non-direct-view area and the direct-view area and the number and installation position of the anchor points deployed on the wall, based on the ray tracing analysis.

18. The device as claimed in claim 10, wherein, The calculation module is configured to calculate and output the ideal anchor point position on the wall based on the ray tracing analysis and user-defined constraints.

19. A computer storage medium, characterized in that, The medium includes instructions that, when executed, perform the three-dimensional environment analysis method as described in any one of claims 1 to 9.

20. A wireless sensor system comprising a three-dimensional environment analysis device as claimed in any one of claims 10 to 18.

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