Laser radar detection method and system for geological disaster identification

Abstract

The invention is suitable for the technical field of geological disaster monitoring, and provides a laser radar detection method and system for geological disaster identification, and the method comprises the following steps: obtaining real-time point cloud data based on a laser radar carried by an unmanned plane, and enabling the unmanned plane to fly according to a set reference route; generating a three-dimensional model based on the real-time point cloud data; synchronously visualizing the three-dimensional model and the reference route, and adjusting the reference route according to the height difference between the three-dimensional model and the reference route; and obtaining point cloud data based on the adjusted reference route. According to the obtained real-time point cloud data, the subsequent route can be planned and adjusted, so that the quality of the obtained point cloud is ensured, and the generation of a landform model can be facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of geological disaster monitoring, and in particular to a laser radar detection method and system for geological disaster identification. Background Art

[0002] With the development of technology, remote sensing technology, drone photography and laser radar technology have been used in the prevention and control of geological disasters. Among them, laser radar can effectively scan the landform by generating high-precision three-dimensional point cloud data by emitting and receiving laser pulses. Through data preprocessing, classification, generation of digital elevation models and extraction of terrain features, it provides support for the analysis and prediction of geological disasters.

[0003] In the existing lidar geological disaster monitoring technology, when dealing with some complex terrains, drones equipped with lidar are usually used for scanning. All subsequent geological analysis and identification are based on the acquired point cloud data, so the accuracy of the point cloud data is very important.

[0004] In actual situations, the point cloud generated by the lidar is characterized by "dense near and sparse far", that is, the landform features at a distance will appear vague. When operating over complex terrain, drones will operate at a certain altitude for safety reasons, which leads to the distance between each area and the lidar being inconsistent, especially in areas with large terrain height differences. This results in incomplete point clouds in some areas. Improving the quality of point clouds through post-processing will deviate from reality. Therefore, a lidar detection method and system for geological disaster identification is proposed to solve the above problems. Summary of the invention

[0005] In view of the deficiencies in the prior art, the purpose of the present invention is to provide a laser radar detection method and system for geological disaster identification to solve the problems existing in the above-mentioned background technology.

[0006] The present invention is implemented as follows: a laser radar detection method for geological disaster identification, the method comprising the following steps:

[0007] Acquiring real-time point cloud data based on a laser radar carried by a drone, the drone flying according to a set reference route;

[0008] Generate 3D models based on real-time point cloud data;

[0009] Synchronously visualizing the three-dimensional model and the reference route and adjusting the reference route according to the height difference between the three-dimensional model and the reference route;

[0010] The point cloud data is acquired based on the adjusted reference route.

[0011] As a further solution of the present invention: the step of generating a three-dimensional model based on real-time point cloud data specifically includes:

[0012] Preprocess the acquired real-time point cloud data;

[0013] Point cloud classification is performed based on the preprocessed real-time point cloud data to distinguish between ground points and non-ground points contained in the point cloud;

[0014] The real-time point cloud data is meshed to generate a 3D model, and the model is optimized.

[0015] As a further solution of the present invention: the step of preprocessing the acquired real-time point cloud data specifically includes:

[0016] Calculate the distance distribution between each point in the real-time point cloud data and the points in the neighborhood to remove the deviated points;

[0017] Align the point cloud based on the NDT algorithm;

[0018] Align the point cloud to the same coordinate system by rotation and translation;

[0019] When the real-time point cloud data comes from multiple sensors, the real-time point cloud data are merged into one point cloud, and duplicate points and overlapping areas are screened out.

[0020] As a further solution of the present invention: the step of synchronously visualizing the three-dimensional model and the reference route and adjusting the reference route according to the height difference between the three-dimensional model and the reference route specifically includes:

[0021] Performing synchronous visualization based on the three-dimensional model and the reference route;

[0022] The three-dimensional model is divided into a near sampling area and a far test area with reference to the position data of the UAV, the heading of the reference route and the sampling range standard, wherein the sampling range standard is the model data of the set span selected in the three-dimensional model according to the direction of the reference route, and the set span is obtained according to the position of the UAV, the density of the point cloud and the height standard of the UAV;

[0023] A height difference distribution set is obtained based on the three-dimensional model of the remote area to be measured and the interval of the reference route corresponding thereto;

[0024] The reference route within the interval is adjusted in altitude according to the altitude difference distribution set.

[0025] As a further solution of the present invention: the step of obtaining a height difference distribution set based on the three-dimensional model of the remote area to be measured and the interval of the reference route corresponding thereto specifically includes:

[0026] The reference route is intercepted according to the three-dimensional model of the remote area to be measured;

[0027] Projecting the intercepted reference route into the three-dimensional model of the remote area to be measured to obtain a guide line, wherein the guide line is distributed along the terrain contour of the remote area to be measured;

[0028] Select the turning point in the guide line and select the corresponding reference point in the intercepted reference route;

[0029] The height difference between each inflection point and the corresponding reference point is calculated to obtain a height difference distribution set.

[0030] As a further solution of the present invention: the step of adjusting the altitude of the reference route within the interval according to the altitude difference distribution set specifically includes:

[0031] The height difference distribution set is screened according to the height standard of the drone to obtain abnormal data, wherein the abnormal data is height difference data in the height difference distribution set that is greater than the height standard of the drone;

[0032] Calculating the difference between the height difference data and the height standard of the UAV to obtain a difference distribution set;

[0033] The height of the position corresponding to the reference heading in the interval is adjusted according to the difference distribution set, so that the density of point cloud data obtained by the drone at the position corresponding to the reference route after adjustment is greater than the density of point cloud data obtained by the original route.

[0034] Another object of the present invention is to provide a laser radar detection system for geological disaster identification, the system comprising:

[0035] A real-time point cloud acquisition module, which acquires real-time point cloud data based on a laser radar carried by a drone, and the drone flies according to a set reference route;

[0036] Point cloud modeling module, which generates 3D models based on real-time point cloud data;

[0037] An analysis module, used for synchronously visualizing the three-dimensional model and the reference route and adjusting the reference route according to the height difference between the three-dimensional model and the reference route;

[0038] The point cloud data collection module acquires point cloud data based on the adjusted reference route.

[0039] As a further solution of the present invention: the point cloud modeling module includes:

[0040] A data preprocessing unit, used for preprocessing the acquired real-time point cloud data;

[0041] A point cloud classification unit performs point cloud classification based on preprocessed real-time point cloud data to distinguish ground points and non-ground points contained in the point cloud;

[0042] The model generation and optimization unit is used to grid the real-time point cloud data to generate a three-dimensional model and perform model optimization.

[0043] As a further solution of the present invention: the analysis module includes:

[0044] A visualization unit, performing synchronous visualization processing based on the three-dimensional model and the reference route;

[0045] A model division unit, for dividing the three-dimensional model into a near sampling area and a far testing area with reference to the position data of the UAV, the heading of the reference route and the sampling range standard, wherein the sampling range standard is the model data of a set span selected in the three-dimensional model according to the direction of the reference route, and the set span is obtained according to the position of the UAV, the density of the point cloud and the height standard of the UAV;

[0046] The height difference analysis unit obtains a height difference distribution set based on a three-dimensional model of a remote area to be measured and an interval of a reference route corresponding thereto;

[0047] The route adjustment unit is used to adjust the altitude of the reference route within the interval according to the altitude difference distribution set.

[0048] As a further solution of the present invention: the height difference analysis unit comprises:

[0049] A route interception subunit is used to intercept the reference route according to the three-dimensional model of the remote area to be measured;

[0050] A route processing subunit, used for projecting the intercepted reference route into a three-dimensional model of a remote area to be measured to obtain a guide line, wherein the guide line is distributed along a terrain contour of the remote area to be measured;

[0051] A point selection subunit is used to select the inflection point in the guide line and select the corresponding reference point in the intercepted reference route;

[0052] The point calculation statistics subunit is used to calculate the height difference between each inflection point and the corresponding reference point to obtain a height difference distribution set.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] The present invention uses a drone equipped with a laser radar to scan geological landforms, and uses the acquired point cloud data to establish a three-dimensional model of the geological landform, thereby facilitating the identification and analysis of geological disasters. For the sake of flight safety, the drone will fly at a certain altitude according to the set reference route. Because the actual terrain is undulating, this leads to incomplete collection of point cloud data in areas far away from the drone, and only the approximate terrain will be displayed in this area. The feature of the present invention is that a three-dimensional model of the terrain can be generated in advance through these point clouds, and then the height difference between the drone and the drone is pre-analyzed through the three-dimensional model, and then the reference route of the drone is adjusted in height. Based on the acquired real-time point cloud data, the subsequent route can be planned and adjusted. This method ensures the quality of the acquired point cloud, which can help generate a model of the terrain and is more conducive to the subsequent identification and processing of geological disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 The figure is a flow chart of a laser radar detection method for geological disaster identification.

[0056] Figure 2 The present invention is a flowchart for generating a three-dimensional model based on real-time point cloud data in a lidar detection method for geological hazard identification.

[0057] Figure 3 The present invention is a flowchart for preprocessing the real-time point cloud data acquired in a laser radar detection method for geological disaster identification.

[0058] Figure 4 The invention discloses a flow chart for synchronously visualizing the three-dimensional model and the reference route and adjusting the reference route according to the height difference between the three-dimensional model and the reference route in a laser radar detection method for geological disaster identification.

[0059] Figure 5 The present invention is a flow chart of obtaining a height difference distribution set based on a three-dimensional model of a remote area to be detected and an interval of a reference route corresponding thereto in a laser radar detection method for geological disaster identification.

[0060] Figure 6 The present invention is a flow chart of adjusting the height of a reference route within an interval according to a height difference distribution set in a laser radar detection method for geological disaster identification.

[0061] Figure 7 The schematic diagram is a structural diagram of a laser radar detection system for geological disaster identification.

[0062] Figure 8 This is a structural schematic diagram of a point cloud modeling module in a laser radar detection system for geological disaster identification.

[0063] Fig. 9The figure is a schematic diagram of the structure of the analysis module in a laser radar detection system for geological disaster identification.

[0064] Fig.10 The figure is a schematic diagram of the structure of a height difference analysis unit in a laser radar detection system for geological disaster identification. DETAILED DESCRIPTION

[0065] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0066] The specific implementation of the present invention is described in detail below in conjunction with specific embodiments.

[0067] like Figure 1 As shown, an embodiment of the present invention provides a laser radar detection method for geological disaster identification, the method comprising the following steps:

[0068] S100, acquiring real-time point cloud data based on a laser radar carried by a drone, the drone flying according to a set reference route;

[0069] S200, generates 3D models based on real-time point cloud data;

[0070] S300, synchronously visualizing the three-dimensional model and the reference route and adjusting the reference route according to the height difference between the three-dimensional model and the reference route;

[0071] S400: acquiring point cloud data based on the adjusted reference route.

[0072] It should be noted that when the laser radar carried by the drone scans, it is generally an area below the drone. These areas are at different distances relative to the drone. When the drone is at a fixed altitude, the point cloud corresponding to the area directly below the drone is undoubtedly denser than that of the distant area. Therefore, the three-dimensional model generated in the area directly below is more specific and accurate. The point cloud in the distant area is relatively sparse, and the generated three-dimensional model only has a rough outline. The terrain, vegetation, etc. only show a surface condition. Although the point cloud in the distant area is not complete, it can still be used to analyze the terrain. It is feasible to adjust the subsequent flight path of the drone by calculating the contour of the terrain.

[0073] In the embodiment of the present invention, the present invention uses a drone equipped with a laser radar to scan geological landforms, and uses the acquired point cloud data to establish a three-dimensional model of the geological landform, so as to facilitate the identification and analysis of geological disasters. For the sake of flight safety, the drone will fly at a certain altitude according to the set reference route. Because the actual terrain is undulating, this leads to incomplete collection of point cloud data in areas far away from the drone, and only the approximate terrain will be displayed in this area. The feature of the present invention is that a three-dimensional model of the terrain can be generated in advance through these point clouds, and then the height difference between the drone and the drone is pre-analyzed through the three-dimensional model, and then the reference route of the drone is adjusted in height. Based on the acquired real-time point cloud data, the subsequent route can be planned and adjusted. This method ensures the quality of the acquired point cloud, which can help generate a model of the terrain and landform, and is more conducive to the subsequent identification and processing of geological disasters.

[0074] like Figure 2 As shown, as a preferred embodiment of the present invention, the step of generating a three-dimensional model based on real-time point cloud data specifically includes:

[0075] S201, preprocessing the acquired real-time point cloud data;

[0076] S202, performing point cloud classification based on the preprocessed real-time point cloud data to distinguish ground points and non-ground points contained in the point cloud;

[0077] S203, gridding the real-time point cloud data to generate a three-dimensional model, and optimizing the model.

[0078] In the embodiment of the present invention, converting real-time point cloud data into a three-dimensional model is a process involving multiple steps, mainly including data preprocessing, point cloud classification, gridding, model optimization, etc. Figure 3 As shown, the preprocessing steps for real-time point cloud data mainly include:

[0079] S211, calculating the distance distribution between each point in the real-time point cloud data and the points in the neighborhood, so as to remove the deviated points;

[0080] S221, aligning the point cloud based on the NDT algorithm;

[0081] S231, aligning the point cloud to the same coordinate system by rotation and translation;

[0082] S241, when the real-time point cloud data comes from multiple sensors, the real-time point cloud data are merged into one point cloud, and duplicate points and overlapping areas are screened out.

[0083] It should be noted that, generally speaking, real-time point cloud data will contain some misidentified points, which often show the characteristics of deviating from the point group. Different neighborhoods represent different terrain features. According to the distance distribution between each point and the points in the neighborhood, the deviated points can be discovered, so that they can be deleted to avoid subsequent impact. This process is the denoising of real-time point cloud data. The NDT algorithm divides the point cloud into multiple voxels and uses Gaussian distribution to represent the point cloud data in each voxel, so as to achieve point cloud alignment. The specific process includes:

[0084] ① Voxel division

[0085] The size of the voxel is determined according to the density and resolution of the point cloud, and then the point cloud space is divided into multiple voxels according to the voxels of the determined size, each of which contains a number of points;

[0086] ②Calculation of Gaussian distribution

[0087] Calculate the Gaussian distribution parameters of each voxel. The Gaussian distribution parameters include the mean μ and the covariance matrix Σ. It should be noted that for each point set {p1, p2, p3, … p n ,} calculate the mean μ, the mean For each point set within each voxel, the covariance matrix Σ is calculated.

[0088]

[0089] ③Match

[0090] Project each point in the source point cloud to the nearest voxel, and calculate the probability of the point in the Gaussian distribution, which can be achieved by an existing Gaussian distribution probability calculation formula;

[0091] ④Calculation transformation

[0092] The gradient of the source point cloud relative to the target point cloud is calculated according to the probability in the above Gaussian distribution, and then the transformation matrix is ​​updated using the gradient descent method to make the source point cloud closer to the target point cloud.

[0093] The NDT algorithm achieves point cloud alignment by dividing the point cloud into multiple voxels and using Gaussian distribution to represent the point cloud data in each voxel. The NDT algorithm is more robust to the density and distribution of the point cloud and is suitable for situations where the point cloud density is uneven.

[0094] like Figure 4 As shown, as a preferred embodiment of the present invention, the steps of synchronously visualizing the three-dimensional model and the reference route and adjusting the reference route according to the height difference between the three-dimensional model and the reference route specifically include:

[0095] S301, performing synchronous visualization processing based on the three-dimensional model and the reference route;

[0096] S302, dividing the three-dimensional model into a near sampling area and a far testing area with reference to the position data of the UAV, the heading of the reference route, and the sampling range standard, wherein the sampling range standard is to select model data of a set span in the three-dimensional model according to the direction of the reference route, and the set span is obtained according to the position of the UAV, the density of the point cloud, and the height standard of the UAV;

[0097] S303, obtaining a height difference distribution set based on the three-dimensional model of the remote area to be measured and the interval of the reference route corresponding thereto;

[0098] S304: Adjust the altitude of the reference route within the interval according to the altitude difference distribution set.

[0099] In the embodiment of the present invention, the three-dimensional model is established based on real-time point cloud data, and the reference route is pre-set. Both are constructed by specific data, so the two can be visualized, and the two will be in the same model. The position data of the drone, the heading of the reference route and the sampling range standard, the position data of the drone represents the position in the current state, and the heading of the reference route also indicates the area that the laser radar needs to scan and measure in the next step. The point cloud acquisition of the laser radar has the characteristics of "dense near and sparse far". Therefore, when the position of the laser radar is determined, only data within a certain range of the point cloud acquired by the laser radar is available, because the point cloud in the distant area is very sparse and cannot form a specific model. Therefore, the near sampling area is the area where point cloud data can be obtained, and the point cloud of the distant area to be tested cannot be used in the subsequent model for mass disaster identification, but these point clouds of the distant area to be tested can be used to identify the approximate terrain in these areas, which provides a basis for the subsequent path analysis of the drone flight.

[0100] like Figure 5 As shown, as a preferred embodiment of the present invention, the step of obtaining a height difference distribution set based on the three-dimensional model of the remote area to be measured and the interval of the reference route corresponding thereto specifically includes:

[0101] S313, intercepting the reference route according to the three-dimensional model of the remote area to be measured;

[0102] S323, projecting the intercepted reference route into the three-dimensional model of the remote area to be measured to obtain a guide line, wherein the guide line is distributed along the terrain contour of the remote area to be measured;

[0103] S333, selecting an inflection point in the guide line and selecting a corresponding reference point in the intercepted reference route;

[0104] S343, calculating the height difference between each inflection point and the corresponding reference point to obtain a height difference distribution set.

[0105] In an embodiment of the present invention, the reference route corresponds to a three-dimensional model. The interval of the reference route that needs to be adjusted can be determined through the remote area to be tested. The guide lines are distributed on the contour of the model, and each turning point is the location where the terrain contour changes. The reference point is on the original route. By calculating the height difference, it can be determined whether this height distance is within the safe flight range of the drone, thereby determining the adjustment direction of the drone. In short, when the altitude of the drone is reduced, the point cloud obtained in the lower area is denser.

[0106] like Figure 6 As shown, as a preferred embodiment of the present invention, the step of adjusting the altitude of the reference route within the interval according to the altitude difference distribution set specifically includes:

[0107] S314, screening the height difference distribution set according to the height standard of the drone to obtain abnormal data, wherein the abnormal data is height difference data in the height difference distribution set that is greater than the height standard of the drone;

[0108] S324, calculating the difference between the height difference data and the height standard of the UAV to obtain a difference distribution set;

[0109] S334, adjusting the height of the position corresponding to the reference heading in the interval according to the difference distribution set, so that the point cloud data density obtained by the drone at the position corresponding to the reference route after adjustment is greater than the point cloud data density obtained by the original route.

[0110] In the embodiment of the present invention, the height standard needs to be tested in advance, and the point cloud data of the terrain is obtained by the laser radar carried by the drone, and the height of the drone is adjusted at the same time, but the safety height of the drone must be guaranteed to ensure that the acquired point cloud data is the most complete, so that the highly applicable point in the reference route can be discovered, and the point also corresponds to the abnormal data. For the convenience of explanation, an example is given. For example, if it is necessary to identify geological disasters in a complex terrain, then it is necessary to first collect point cloud data of this area. In order to fly safely, the drone equipped with a laser radar will perform horizontal flight collection at a certain height. This method is simpler to obtain point clouds, but in actual situations, there may be valleys, gullies and other terrain features in this area. The distance between these areas and the drone is greatly increased, which may affect the quality of the point cloud. The present invention collects these terrain features in advance, and then adjusts the flight altitude of the drone in a targeted manner, so that the drone can actively lower the flight altitude when passing through these areas, thereby ensuring the integrity of the acquired point cloud data.

[0111] like Figure 7As shown, an embodiment of the present invention further provides a laser radar detection system for geological disaster identification, the system comprising:

[0112] A real-time point cloud acquisition module 100 acquires real-time point cloud data based on a laser radar carried by a drone, the drone flying according to a set reference route;

[0113] Point cloud modeling module 200, generating a three-dimensional model based on real-time point cloud data;

[0114] An analysis module 300 is used to simultaneously visualize the three-dimensional model and the reference route and adjust the reference route according to the height difference between the three-dimensional model and the reference route;

[0115] The point cloud data collection module 400 acquires point cloud data based on the adjusted reference route.

[0116] In the embodiment of the present invention, the present invention uses a drone equipped with a laser radar to scan geological landforms, and uses the acquired point cloud data to establish a three-dimensional model of the geological landform, so as to facilitate the identification and analysis of geological disasters. For the sake of flight safety, the drone will fly at a certain altitude according to the set reference route. Because the actual terrain is undulating, this leads to incomplete collection of point cloud data in areas far away from the drone, and only the approximate terrain will be displayed in this area. The feature of the present invention is that a three-dimensional model of the terrain can be generated in advance through these point clouds, and then the height difference between the drone and the drone is pre-analyzed through the three-dimensional model, and then the reference route of the drone is adjusted in height. Based on the acquired real-time point cloud data, the subsequent route can be planned and adjusted. This method ensures the quality of the acquired point cloud, which can help generate a model of the terrain and landform, and is more conducive to the subsequent identification and processing of geological disasters.

[0117] like Figure 8 As shown, as a preferred embodiment of the present invention, the point cloud modeling module 200 includes:

[0118] A data preprocessing unit 201 is used to preprocess the acquired real-time point cloud data;

[0119] A point cloud classification unit 202 performs point cloud classification based on the preprocessed real-time point cloud data to distinguish between ground points and non-ground points contained in the point cloud;

[0120] The model generation and optimization unit 203 is used to grid the real-time point cloud data to generate a three-dimensional model and perform model optimization.

[0121] like Fig. 9 As shown, as a preferred embodiment of the present invention, the analysis module 300 includes:

[0122] A visualization unit 301 performs synchronous visualization processing based on the three-dimensional model and the reference route;

[0123] The model division unit 302 is used to divide the three-dimensional model into a near sampling area and a far area to be measured by referring to the position data of the UAV, the heading of the reference route and the sampling range standard, wherein the sampling range standard is to select model data of a set span in the three-dimensional model according to the direction of the reference route, and the set span is obtained according to the position of the UAV, the density of the point cloud and the height standard of the UAV;

[0124] The height difference analysis unit 303 obtains a height difference distribution set based on the three-dimensional model of the remote area to be measured and the interval of the reference route corresponding thereto;

[0125] The route adjustment unit 304 is used to adjust the altitude of the reference route within the interval according to the altitude difference distribution set.

[0126] like Fig.10 As shown, as a preferred embodiment of the present invention, the height difference analysis unit 303 includes:

[0127] The route interception subunit 313 is used to intercept the reference route according to the three-dimensional model of the remote area to be measured;

[0128] The route processing subunit 323 is used to project the intercepted reference route into the three-dimensional model of the remote area to be measured to obtain a guide line, wherein the guide line is distributed along the terrain contour of the remote area to be measured;

[0129] A point selection subunit 333 is used to select an inflection point in the guide line and select a corresponding reference point in the intercepted reference route;

[0130] The point calculation and statistics subunit 343 is used to calculate the height difference between each inflection point and the corresponding reference point to obtain a height difference distribution set.

[0131] The above only describes in detail the preferred embodiments of the present invention, which is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

[0132] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0133] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0134] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the disclosure in the specification and examples. This application is intended to cover any variations, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the claims.

Claims

1. A laser radar detection method for geological disaster identification, characterized in that: The method comprises the following steps: Acquiring real-time point cloud data based on a laser radar carried by a drone, the drone flying according to a set reference route; Generate 3D models based on real-time point cloud data; Synchronously visualizing the three-dimensional model and the reference route and adjusting the reference route according to the height difference between the three-dimensional model and the reference route; The point cloud data is acquired based on the adjusted reference route.

2. The laser radar detection method for geological disaster identification according to claim 1 is characterized in that: The step of generating a three-dimensional model based on real-time point cloud data specifically includes: Preprocess the acquired real-time point cloud data; Point cloud classification is performed based on the preprocessed real-time point cloud data to distinguish between ground points and non-ground points contained in the point cloud; The real-time point cloud data is meshed to generate a 3D model, and the model is optimized.

3. The laser radar detection method for geological disaster identification according to claim 2 is characterized in that: The step of preprocessing the acquired real-time point cloud data specifically includes: Calculate the distance distribution between each point in the real-time point cloud data and the points in the neighborhood to remove the deviated points; Align the point cloud based on the NDT algorithm; Align the point cloud to the same coordinate system by rotation and translation; When the real-time point cloud data comes from multiple sensors, the real-time point cloud data are merged into one point cloud, and duplicate points and overlapping areas are screened out.

4. The laser radar detection method for geological disaster identification according to claim 1 is characterized in that: The step of synchronously visualizing the three-dimensional model and the reference route and adjusting the reference route according to the height difference between the three-dimensional model and the reference route specifically includes: Performing synchronous visualization based on the three-dimensional model and the reference route; The three-dimensional model is divided into a near sampling area and a far test area with reference to the position data of the UAV, the heading of the reference route and the sampling range standard, wherein the sampling range standard is the model data of the set span selected in the three-dimensional model according to the direction of the reference route, and the set span is obtained according to the position of the UAV, the density of the point cloud and the height standard of the UAV; A height difference distribution set is obtained based on the three-dimensional model of the remote area to be measured and the interval of the reference route corresponding thereto; The reference route within the interval is adjusted in altitude according to the altitude difference distribution set.

5. The laser radar detection method for geological disaster identification according to claim 4 is characterized in that: The step of obtaining a height difference distribution set based on the three-dimensional model of the remote area to be measured and the interval of the reference route corresponding thereto specifically includes: The reference route is intercepted according to the three-dimensional model of the remote area to be measured; Projecting the intercepted reference route into the three-dimensional model of the remote area to be measured to obtain a guide line, wherein the guide line is distributed along the terrain contour of the remote area to be measured; Select the turning point in the guide line and select the corresponding reference point in the intercepted reference route; The height difference between each inflection point and the corresponding reference point is calculated to obtain a height difference distribution set.

6. The laser radar detection method for geological disaster identification according to claim 4 is characterized in that: The step of adjusting the altitude of the reference route within the interval according to the altitude difference distribution set specifically includes: The height difference distribution set is screened according to the height standard of the drone to obtain abnormal data, wherein the abnormal data is height difference data in the height difference distribution set that is greater than the height standard of the drone; Calculating the difference between the height difference data and the height standard of the UAV to obtain a difference distribution set; The height of the position corresponding to the reference heading in the interval is adjusted according to the difference distribution set, so that the density of point cloud data obtained by the drone at the position corresponding to the reference route after adjustment is greater than the density of point cloud data obtained by the original route.

7. A laser radar detection system for geological disaster identification, characterized in that: The system comprises: A real-time point cloud acquisition module, which acquires real-time point cloud data based on a laser radar carried by a drone, and the drone flies according to a set reference route; Point cloud modeling module, which generates 3D models based on real-time point cloud data; An analysis module, used for synchronously visualizing the three-dimensional model and the reference route and adjusting the reference route according to the height difference between the three-dimensional model and the reference route; The point cloud data collection module acquires point cloud data based on the adjusted reference route.

8. The laser radar detection system for geological disaster identification according to claim 7 is characterized in that: The point cloud modeling module includes: A data preprocessing unit, used for preprocessing the acquired real-time point cloud data; A point cloud classification unit performs point cloud classification based on preprocessed real-time point cloud data to distinguish ground points and non-ground points contained in the point cloud; The model generation and optimization unit is used to grid the real-time point cloud data to generate a three-dimensional model and perform model optimization.

9. The laser radar detection system for geological disaster identification according to claim 7 is characterized in that: The analysis module comprises: A visualization unit, performing synchronous visualization processing based on the three-dimensional model and the reference route; A model division unit, for dividing the three-dimensional model into a near sampling area and a far testing area with reference to the position data of the UAV, the heading of the reference route and the sampling range standard, wherein the sampling range standard is the model data of a set span selected in the three-dimensional model according to the direction of the reference route, and the set span is obtained according to the position of the UAV, the density of the point cloud and the height standard of the UAV; The height difference analysis unit obtains a height difference distribution set based on a three-dimensional model of a remote area to be measured and an interval of a reference route corresponding thereto; The route adjustment unit is used to adjust the altitude of the reference route within the interval according to the altitude difference distribution set.

10. The laser radar detection system for geological disaster identification according to claim 9, characterized in that: The height difference analysis unit comprises: A route interception subunit is used to intercept the reference route according to the three-dimensional model of the remote area to be measured; A route processing subunit, used for projecting the intercepted reference route into a three-dimensional model of a remote area to be measured to obtain a guide line, wherein the guide line is distributed along a terrain contour of the remote area to be measured; A point selection subunit is used to select the inflection point in the guide line and select the corresponding reference point in the intercepted reference route; The point calculation statistics subunit is used to calculate the height difference between each inflection point and the corresponding reference point to obtain a height difference distribution set.

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