An efficient surveying and mapping system based on point cloud data

Abstract

The present invention relates to the field of surveying and mapping technology, and specifically discloses an efficient surveying and mapping system based on point cloud data, including: a division module: gridding the surveying and mapping area, determining surveying and mapping points, and obtaining point cloud data in the grid area at the surveying and mapping points; a processing module: obtaining target information of a drone, determining a target polyhedron based on the target information and the coordinates in the central coordinate system of the drone; obtaining polyhedron parameters, and obtaining polyhedron vectors according to the polyhedron parameters; a decision module: periodically obtaining polyhedron vectors, determining reference vectors and comparison vectors; determining the similarity between the reference vector and the comparison vector, determining an abnormal area based on the similarity, and calibrating the point cloud data corresponding to the abnormal area. The present invention can determine the grid area where point cloud data needs to be calibrated, thereby reducing the time required to calibrate point cloud data and improving the efficiency of surveying and mapping.

Description

Technical Field

[0001] The present invention relates to the field of surveying and mapping technology, and in particular to an efficient surveying and mapping system based on point cloud data. Background Art

[0002] Point cloud is a data set composed of a large number of discrete points in space, which is used to represent the three-dimensional shape and features of an object. These points are usually acquired by sensors. Surveying and mapping is the science and technology of acquiring, processing and expressing spatial information of the earth's surface or other objects. Point cloud provides high-precision and high-density spatial data, which has brought breakthrough changes to surveying and mapping work.

[0003] With the development of science and technology, the method of surveying and mapping by drones has gradually become popular and widely used. However, during the surveying and mapping process, the attitude of the drone (including pitch angle, yaw angle and roll angle) will directly affect the accuracy of the acquired point cloud data. Specifically, the change of the drone's attitude will cause the viewing angle of the camera or lidar to shift, thereby affecting the spatial distribution of the point cloud points.

[0004] In this case, the point cloud needs to be calibrated to correct the deviation caused by the change of the drone's attitude. However, the calibration process often requires complex algorithm support. When facing high-density or large-scale point cloud data, it will take more time and computing resources to calibrate, which will have an adverse effect on the efficiency of surveying and mapping. Based on this, an efficient surveying and mapping system based on point cloud data is proposed to improve the efficiency of surveying and mapping. Summary of the invention

[0005] The purpose of the present invention is to provide an efficient surveying and mapping system based on point cloud data to solve the above technical problems.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] An efficient surveying and mapping system based on point cloud data includes a division module, a processing module and a decision module, specifically:

[0008] Division module: divide the preset surveying and mapping area into grids to obtain a grid area, wherein the length and width of a single grid are both preset values, and the center point of the grid area is used as the surveying and mapping point, and the point cloud data in the grid area is obtained at the surveying and mapping point based on the surveying and mapping sensor carried by the UAV;

[0009] Processing module: obtain the target information of the drone, which includes the pitch angle target arc D fy +1, Yaw angle target arc D ph +1 and roll angle target radians D fg +1, D fy , D ph , D fgThey represent the pitch angle radian, yaw angle radian, and roll angle radian of the drone respectively;

[0010] Determine the coordinates AZB of the center of the drone in the preset coordinate system A, and convert the pitch angle target arc D fy +1, Yaw angle target arc D ph +1, Roll angle target arc D fg +1 The corresponding points on the X-axis, Y-axis, and Z-axis of the coordinate system A are taken as reference points, the reference points are connected, and the reference points and the coordinates AZB are connected to obtain the target polyhedron;

[0011] Obtaining polyhedron parameters based on the target polyhedron, the polyhedron parameters including the volume V of the polyhedron, the area SFV of the front view of the polyhedron, the area STV of the top view of the polyhedron, and the area SSV of the side view of the polyhedron, and determining the polyhedron vector (V, SFV, STV, SSV) based on the polyhedron parameters;

[0012] Decision module: periodically obtain polyhedral vectors within a preset monitoring period, sort the polyhedral vectors according to the time axis order, use the first polyhedral vector in the sorting as a reference vector, and use the polyhedral vectors in the remaining positions in the sorting as comparison vectors;

[0013] The similarity between the reference vector and the comparison vector is determined, an abnormal area is determined based on the similarity, and point cloud data corresponding to the abnormal area is calibrated.

[0014] As a further solution of the present invention: in the decision module, the process of determining the similarity between the reference vector and the comparison vector specifically includes:

[0015] The cosine value of the angle between the reference vector and the comparison vector is calculated, and the cosine value of the angle is used as the similarity P.

[0016] As a further solution of the present invention: in the decision module, the process of determining the grid area for calibrating the point cloud data specifically includes:

[0017] Determine the minimum similarity P min =min(PJH),PJH=(P1,P2,…,P n-1 ), P n-1 represents the similarity between the n-1th comparison vector and the reference vector, and n represents the total number of polyhedral vectors obtained during the monitoring period;

[0018] Set the similarity threshold P ys , when the similarity is the minimum similarity P min <P ys When the corresponding grid area is taken as the abnormal area, the point cloud data in the abnormal area is calibrated.

[0019] As a further solution of the present invention: when the ratio of the number of abnormal areas to the total number of grid areas is greater than or equal to 0.7, an early warning message is sent to remind the management personnel.

[0020] As a further solution of the present invention: in the processing module, the pitch angle radian, yaw angle radian, and roll angle radian are determined based on the pitch angle, yaw angle, and roll angle respectively, and the pitch angle, yaw angle, and roll angle are acquired based on sensors.

[0021] As a further solution of the present invention: in the division module, the length and width of a single grid are set based on the accuracy of the surveying and mapping sensor. The higher the accuracy of the surveying and mapping sensor, the larger the length and width of the single grid.

[0022] Beneficial effects of the present invention: In this solution, grid division ensures comprehensive coverage of the surveying area and avoids missing any area. At the same time, the center point of the grid is used as the surveying point to ensure that the grid area can be evenly covered, and finally ensure that the point cloud data covers all areas; then the target information of the UAV is determined; it can be understood that the attitude information of the UAV is crucial for accurate surveying and data calibration. By obtaining the target information through the attitude information, the attitude changes of the UAV can be analyzed to provide a data basis for subsequent steps; then, the target polyhedron is obtained. The shape of the target polyhedron can intuitively represent the attitude and motion state of the UAV. By calculating the polyhedron parameters, the complex spatial motion can be quantified into comparable data; by generating a polyhedron and determining the polyhedron parameters, the attitude information is converted into quantitative data, which is convenient for subsequent comparison and detection, and according to the polyhedron The polyhedron vector is obtained by using the face parameters, and the complex posture information is simplified into comparable vector data; finally, the reference vector and the comparison vector are determined; it should be noted that the monitoring period is the time point from when the mapping of a single grid area starts to the time point when the mapping in a single grid area ends, so the first reference vector in the sorting is the polyhedron vector corresponding to the drone when the mapping of the grid area starts, and the abnormal area is subsequently determined by the similarity between the reference vector and the comparison vector. The smaller the similarity, the more the posture of the drone changes compared to the initial posture of the drone. Therefore, it is more necessary to calibrate the point cloud data corresponding to the grid area to ensure the accuracy of the point cloud data, and only the point cloud data of the abnormal area needs to be calibrated, which can reduce the time cost required to calibrate the point cloud data of all grid areas, thereby improving the efficiency of mapping. The present invention can determine the grid area where the point cloud data needs to be calibrated, thereby reducing the time required to calibrate the point cloud data and improving the efficiency of mapping. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The present invention will be further described below in conjunction with the accompanying drawings.

[0024] Figure 1 It is a flow chart of an efficient surveying and mapping system based on point cloud data of the present invention. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] See also Figure 1 As shown, the present invention is an efficient surveying and mapping system based on point cloud data, including a division module, a processing module and a decision module, specifically:

[0027] Division module: divide the preset surveying and mapping area into grids to obtain a grid area, wherein the length and width of a single grid are both preset values, and the center point of the grid area is used as the surveying and mapping point, and the point cloud data in the grid area is obtained at the surveying and mapping point based on the surveying and mapping sensor carried by the UAV;

[0028] Processing module: obtain the target information of the drone, which includes the pitch angle target arc D fy +1, Yaw angle target arc D ph +1 and roll angle target radians D fg +1, D fy , D ph , D fg They represent the pitch angle radian, yaw angle radian, and roll angle radian of the drone respectively;

[0029] Determine the coordinates AZB of the center of the drone in the preset coordinate system A, and convert the pitch angle target arc D fy +1, Yaw angle target arc D ph +1, Roll angle target arc D fg +1 The corresponding points on the X-axis, Y-axis, and Z-axis of the coordinate system A are taken as reference points, the reference points are connected, and the reference points and the coordinates AZB are connected to obtain the target polyhedron;

[0030] Obtaining polyhedron parameters based on the target polyhedron, the polyhedron parameters including the volume V of the polyhedron, the area SFV of the front view of the polyhedron, the area STV of the top view of the polyhedron, and the area SSV of the side view of the polyhedron, and determining the polyhedron vector (V, SFV, STV, SSV) based on the polyhedron parameters;

[0031] Decision module: periodically obtain polyhedral vectors within a preset monitoring period, sort the polyhedral vectors according to the time axis order, use the first polyhedral vector in the sorting as a reference vector, and use the polyhedral vectors in the remaining positions in the sorting as comparison vectors;

[0032] The similarity between the reference vector and the comparison vector is determined, an abnormal area is determined based on the similarity, and point cloud data corresponding to the abnormal area is calibrated.

[0033] It should be noted that the grid division ensures the comprehensive coverage of the surveying area and avoids missing any area. At the same time, the grid center point is used as the surveying point to ensure that the grid area can be evenly covered, and finally ensure that the point cloud data covers all areas. Then the target information of the UAV is determined. It can be understood that the attitude information of the UAV is crucial for accurate surveying and data calibration. By obtaining the target information through the attitude information, the attitude changes of the UAV can be analyzed to provide a data basis for subsequent steps. Then, the target polyhedron is obtained. The shape of the target polyhedron can intuitively represent the attitude and motion state of the UAV. By calculating the polyhedron parameters, the complex spatial motion can be quantified into comparable data. By generating a polyhedron and determining the polyhedron parameters, the attitude information is converted into quantitative data, which is convenient for subsequent comparison and detection, and according to the polyhedron parameters The polyhedron vector is obtained, and the complex posture information is simplified into comparable vector data; finally, the reference vector and the comparison vector are determined; it should be noted that the monitoring period is from the time point when the mapping of a single grid area starts to the time point when the mapping in a single grid area ends. Therefore, the first reference vector in the sorting is the polyhedron vector corresponding to the drone when the mapping of the grid area starts. Subsequently, the abnormal area is determined by the similarity between the reference vector and the comparison vector. The smaller the similarity, the more the posture of the drone has changed compared to the initial posture of the drone. Therefore, it is more necessary to calibrate the point cloud data corresponding to the grid area to ensure the accuracy of the point cloud data. In addition, only the point cloud data of the abnormal area needs to be calibrated, which can reduce the time cost required to calibrate the point cloud data of all grid areas, thereby improving the efficiency of mapping.

[0034] In another preferred embodiment of the present invention, in the decision module, the process of determining the similarity between the reference vector and the comparison vector specifically includes:

[0035] The cosine value of the angle between the reference vector and the comparison vector is calculated, and the cosine value of the angle is used as the similarity P.

[0036] It is worth noting that the cosine value of the angle is one of the commonly used indicators for calculating the similarity between two vectors, called cosine similarity. It determines the similarity between two vectors by calculating the cosine value of the angle between them. If the cosine value of the angle is close to 1, it means that the directions and sizes of the two vectors are similar, and the drone's attitude has not changed significantly. If the cosine value of the angle is small or close to 0 or a negative value, it means that the directions of the two vectors have changed significantly and the drone's attitude has changed significantly. The specific calculation method will not be repeated here.

[0037] In another preferred embodiment of the present invention, in the decision module, the process of determining the grid area for calibrating the point cloud data specifically includes:

[0038] Determine the minimum similarity P min =min(PJH),PJH=(P1,P2,…,P n-1 ), P n-1 represents the similarity between the n-1th comparison vector and the reference vector, and n represents the total number of polyhedral vectors obtained during the monitoring period;

[0039] Set the similarity threshold P ys , when the similarity is the minimum similarity P min <P ys When the corresponding grid area is taken as the abnormal area, the point cloud data in the abnormal area is calibrated.

[0040] It is understandable that the minimum similarity reflects the extreme situation of posture change in the entire mapping process. If the posture deviation at a certain moment is large, its similarity with the reference vector will be significantly reduced, resulting in a smaller minimum similarity. By locating the minimum value, it is possible to identify whether the posture change exceeds expectations.

[0041] In another preferred embodiment of the present invention, when the ratio of the number of abnormal areas to the total number of grid areas is greater than or equal to 0.7, an early warning message is sent to alert the management personnel.

[0042] It should be noted that the process of calibrating point cloud data involves processing the data of each abnormal area one by one, which may require complex calibration calculations and multiple data corrections. Especially in the case of large-scale anomalies, the computational burden of such calibration will become very heavy, and the accuracy of the calibrated data may still not meet the requirements. In the case of large-scale anomalies, re-surveying is usually more efficient than full calibration. The drone only needs to re-arrive at the surveying point and survey again. There is no need to calibrate the point cloud data in multiple abnormal areas one by one. Therefore, an early warning message is sent to remind the management personnel, who make decisions.

[0043] In another preferred embodiment of the present invention, in the processing module, the pitch angle radian, yaw angle radian, and roll angle radian are determined based on the pitch angle, yaw angle, and roll angle, respectively, and the pitch angle, yaw angle, and roll angle are acquired based on sensors.

[0044] It should be noted that, taking the pitch angle radian as an example, the pitch angle F obtained based on the sensor is recorded, then the pitch angle radian FHD=(π / 180)×F; the process of obtaining the yaw angle radian and the roll angle radian can refer to the pitch angle radian, which will not be repeated here.

[0045] In another preferred embodiment of the present invention, in the division module, the length and width of a single grid are set based on the accuracy of the surveying and mapping sensor. The higher the accuracy of the surveying and mapping sensor, the larger the length and width of the single grid.

[0046] It is understandable that high-precision mapping sensors can maintain the ability to accurately collect point cloud data at longer distances and larger areas, which means that even if the grid size is increased, the sensor can still collect enough detailed information to ensure the integrity and accuracy of the data; if the sensor accuracy is low, a smaller grid is required to ensure sufficient data coverage, otherwise data omissions or error accumulation may occur due to insufficient sampling density; the specific size of the grid can be pre-set by the staff.

[0047] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. An efficient surveying and mapping system based on point cloud data, characterized in that: It includes a division module, a processing module and a decision module, specifically: Division module: divide the preset surveying and mapping area into grids to obtain a grid area, wherein the length and width of a single grid are both preset values, and the center point of the grid area is used as the surveying and mapping point, and the point cloud data in the grid area is obtained at the surveying and mapping point based on the surveying and mapping sensor carried by the UAV; Processing module: obtain the target information of the drone, which includes the pitch angle target arc D fy +1, Yaw angle target arc D ph +1 and roll angle target radians D fg +1, D fy , D ph , D fg They represent the pitch angle radian, yaw angle radian, and roll angle radian of the drone respectively; Determine the coordinates AZB of the center of the drone in the preset coordinate system A, and convert the pitch angle target arc D fy +1, Yaw angle target arc D ph +1, Roll angle target arc D fg +1 The corresponding points on the X-axis, Y-axis, and Z-axis of the coordinate system A are taken as reference points, the reference points are connected, and the reference points and the coordinates AZB are connected to obtain the target polyhedron; Obtaining polyhedron parameters based on the target polyhedron, the polyhedron parameters including the volume V of the polyhedron, the area SFV of the front view of the polyhedron, the area STV of the top view of the polyhedron, and the area SSV of the side view of the polyhedron, and determining the polyhedron vector (V, SFV, STV, SSV) based on the polyhedron parameters; Decision module: periodically obtain polyhedral vectors within a preset monitoring period, sort the polyhedral vectors according to the time axis order, use the first polyhedral vector in the sorting as a reference vector, and use the polyhedral vectors in the remaining positions in the sorting as comparison vectors; The similarity between the reference vector and the comparison vector is determined, an abnormal area is determined based on the similarity, and point cloud data corresponding to the abnormal area is calibrated.

2. According to claim 1, an efficient surveying and mapping system based on point cloud data is characterized in that: In the decision module, the process of determining the similarity between the reference vector and the comparison vector specifically includes: The cosine value of the angle between the reference vector and the comparison vector is calculated, and the cosine value of the angle is used as the similarity P.

3. The efficient surveying and mapping system based on point cloud data according to claim 2, characterized in that: In the decision module, the process of determining the grid area for calibrating the point cloud data specifically includes: Determine the minimum similarity P min =min(PJH),PJH=(P1,P2,…,P n-1 ), P n-1 represents the similarity between the n-1th comparison vector and the reference vector, and n represents the total number of polyhedral vectors obtained during the monitoring period; Set the similarity threshold P ys , when the similarity is the minimum similarity P min <P ys When the corresponding grid area is taken as the abnormal area, the point cloud data in the abnormal area is calibrated.

4. The efficient surveying and mapping system based on point cloud data according to claim 3, characterized in that: When the ratio of the number of abnormal areas to the total number of grid areas is greater than or equal to 0.7, an early warning message is sent to remind the management personnel.

5. The efficient surveying and mapping system based on point cloud data according to claim 1, characterized in that: In the processing module, the pitch angle radian, the yaw angle radian, and the roll angle radian are determined based on the pitch angle, the yaw angle, and the roll angle, respectively, and the pitch angle, the yaw angle, and the roll angle are acquired based on a sensor.

6. The efficient surveying and mapping system based on point cloud data according to claim 1, characterized in that: In the division module, the length and width of a single grid are set based on the accuracy of the surveying and mapping sensor. The higher the accuracy of the surveying and mapping sensor, the larger the length and width of the single grid.

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