A method for modeling point clouds of obstacles on the surface of extraterrestrial objects

Abstract

A method for modeling point cloud of obstacles on the surface of extraterrestrial bodies belongs to the field of image processing and intelligent control technology. The invention uses hemispheres and semi-ellipsoids to simulate craters and rocks respectively, and after generating the model, it is superimposed on the base plane; then a certain degree of Gaussian noise is superimposed on the generated plane, and then the sensor noise is analyzed and extracted, and mask processing is performed on the obstacle image, and it is further superimposed on the image to generate the final simulation image.

Description

Technical Field

[0001] The invention relates to a point cloud modeling method for an extraterrestrial body surface obstacle, and belongs to the technical field of image processing and intelligent control. Background Art

[0002] During the landing process of the extraterrestrial exploration mission, autonomous obstacle avoidance will be one of the main technical approaches. Simulation tests of various working conditions on the ground are the main technical means to ensure the effectiveness of the obstacle avoidance algorithm. At present, the sources of data at home and abroad are either to build a local real-life terrain sand table on the ground, or to use scattered celestial body pictures obtained from successful celestial body exploration missions. The limitations of the real-life sand table are that the scene area itself is small and its application is limited; and the scene is single, and the location and number of obstacles are difficult to have a relatively comprehensive coverage; at the same time, the sand table is also difficult to ensure the proportional size of each terrain feature. For example, the actual crater diameter is large, which is difficult to achieve on the sand table. The scattered celestial body data pictures obtained from previous exploration missions are difficult to ensure systematicity. At the same time, most of them are optical images, and three-dimensional point cloud data are very scarce. In addition to constructing local real-life terrain, a feasible method is to simulate and generate obstacle point clouds on the surface of celestial bodies by itself. Pure simulation images tend to ignore other image problems except Gaussian noise, such as the lack of return light and irregular noise caused by the scene illumination, ground reflectivity, laser energy and other problems during the scanning process of the laser radar, which is not enough to fully verify the effectiveness and robustness of the algorithm. Summary of the invention

[0003] The technical problem solved by the present invention is: to overcome the deficiencies of the prior art, to provide a method for modeling an extraterrestrial body surface obstacle point cloud, to solve the problem that there are insufficient test cases for extraterrestrial body landing three-dimensional point cloud data, and that the simulation data does not fully consider various types of noise of the sensor, and is insufficient to fully verify the effectiveness and robustness of the landing obstacle avoidance algorithm.

[0004] The technical solution of the present invention is: a method for modeling point cloud of obstacles on the surface of an extraterrestrial body, comprising the following steps:

[0005] Generate a basic plane model superimposed with obstacles on the surface of an extraterrestrial body;

[0006] Determine the types of obstacles on the surface of extraterrestrial bodies and the models for simulating various obstacles, and generate an obstacle database;

[0007] Determine the center point of the obstacle on the basic plane model, select the obstacle from the obstacle database, accumulate the elevation values ​​according to the corresponding positions of the XY coordinates, and generate a three-dimensional point cloud with the obstacle superimposed;

[0008] A noise model that simulates the noise generated by the lidar during the scanning process is added to the three-dimensional point cloud to complete the point cloud modeling of obstacles on the surface of extraterrestrial bodies.

[0009] Furthermore, the generation of a basic plane model for superimposing obstacles on the surface of an extraterrestrial body includes the following steps: arranging the plane point clouds at equal intervals, presetting the plane height Z, and generating a basic plane point cloud with the coordinates (0,0,Z) as the center.

[0010] Furthermore, the generated basic plane is a plane with a certain slope, and the data format is a normalized three-dimensional point cloud.

[0011] Furthermore, the types of obstacles on the surface of the extraterrestrial body include craters and rocks, which are simulated using a hemispherical model and a semi-ellipsoid, respectively.

[0012] Furthermore, the generating of the obstacle database comprises the following steps:

[0013] Investigate the size and density distribution of rocks and craters on the surface of celestial bodies, determine the depth-to-width ratio of craters, the ratio of rock height to bottom width, determine point intervals, and generate local point clouds;

[0014] Generate a hemispherical model: take the center of the hemispherical interface as the origin O, the cross section coincides with the XOY plane, and the crater mouth faces upward; determine the parametric equation of the corresponding hemisphere based on the depth-to-width ratio, and determine the depth of each point in the crater under the condition of consistent point spacing;

[0015] Generate a semi-ellipsoid model: When generating rocks, assume that the cross section of the ellipsoid is a circle, with the center of the cross section as the origin O, the cross section coincides with the XOY plane, and the ellipsoid is facing upwards; determine the ellipsoid parameter equation of the corresponding rock by the depth-to-width ratio, determine the horizontal and vertical coordinates of each point in the rock by the above point interval, substitute them into the ellipsoid equation, and calculate the height;

[0016] The height and depth of generated crater and rock models are stored in their shapes.

[0017] Furthermore, the noise model includes Gaussian white noise and point cloud data error noise caused by insufficient return light at the edge of the field of view.

[0018] An extraterrestrial body surface obstacle point cloud modeling system, comprising:

[0019] The first module is used to generate a basic plane model superimposed with obstacles on the surface of an extraterrestrial body;

[0020] The second module is used to determine the types of obstacles on the surface of extraterrestrial bodies and the models for simulating various obstacles, and to generate an obstacle database;

[0021] The third module is used to determine the center point of the obstacle on the basic plane model, select the obstacle from the obstacle database, accumulate the elevation value according to the corresponding position of the XY coordinates, and generate a three-dimensional point cloud after superimposing the obstacle;

[0022] The fourth module is used to add a noise model to the three-dimensional point cloud to simulate the noise generated by the laser radar during the scanning process.

[0023] Furthermore, the generation of the basic plane model superimposed with the extraterrestrial body surface obstacles comprises the following steps: arranging the plane point clouds at equal intervals, presetting the plane height Z, and generating the basic plane point cloud with the coordinates (0,0,Z) as the center;

[0024] The generated basic plane is a plane with a certain slope, and the data format is a normalized three-dimensional point cloud;

[0025] The types of obstacles on the surface of the extraterrestrial body include craters and rocks, which are simulated using a hemispherical model and a semi-ellipsoid respectively;

[0026] The generation of the obstacle database comprises the following steps:

[0027] Investigate the size and density distribution of rocks and craters on the surface of celestial bodies, determine the depth-to-width ratio of craters, the ratio of rock height to bottom width, determine point intervals, and generate local point clouds;

[0028] Generate a hemispherical model: take the center of the hemispherical interface as the origin O, the cross section coincides with the XOY plane, and the crater mouth faces upward; determine the parametric equation of the corresponding hemisphere based on the depth-to-width ratio, and determine the depth of each point in the crater under the condition of consistent point spacing;

[0029] Generate a semi-ellipsoid model: When generating rocks, assume that the cross section of the ellipsoid is a circle, with the center of the cross section as the origin O, the cross section coincides with the XOY plane, and the ellipsoid is facing upwards; determine the ellipsoid parameter equation of the corresponding rock by the depth-to-width ratio, determine the horizontal and vertical coordinates of each point in the rock by the above point interval, substitute them into the ellipsoid equation, and calculate the height;

[0030] Store the height and depth of the generated crater and rock models according to their shapes;

[0031] The noise model includes Gaussian white noise and point cloud data error noise caused by insufficient return light at the edge of the field of view.

[0032] A computer-readable storage medium stores a computer program, wherein the computer program implements the steps of the method when executed by a processor.

[0033] A device for modeling a point cloud of obstacles on the surface of an extraterrestrial body, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of a method for modeling a point cloud of obstacles on the surface of an extraterrestrial body when executing the computer program.

[0034] The advantages of the present invention compared with the prior art are:

[0035] The present invention models typical terrain obstacles (craters and rocks), generates a three-dimensional surface image according to the obstacle distribution characteristics of different regions, and can obtain a large amount of three-dimensional point cloud data of celestial terrain. At the same time, other image problems other than Gaussian noise are fully considered, such as the lack of return light and irregular noise caused by the laser radar during the scanning process due to scene illumination, ground reflectivity, laser energy and other issues, which solves the problems of high cost and small data volume of real-scene imaging, and also makes the data as real and effective as possible, ensuring that the effectiveness and robustness of the algorithm are fully tested and verified. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 Generate flow charts for obstacle terrain;

[0037] Figure 2 This is a schematic diagram of the crater simulation;

[0038] Figure 3 This is a schematic diagram of rock simulation;

[0039] Figure 4 This is the simulation diagram after the obstacle and noise are superimposed—XY perspective;

[0040] Figure 5 This is an example of a simulation image after the final obstacle and noise are superimposed - stereoscopic perspective. The final simulation image is superimposed with simulated obstacles (craters, rocks) and sensor noise, including the missing parts of the point cloud. DETAILED DESCRIPTION

[0041] In order to better understand the above technical scheme, the technical scheme of the present application is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical scheme of the present application, rather than limitations on the technical scheme of the present application. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0042] The following is a further detailed description of a method for modeling an extraterrestrial body surface obstacle point cloud provided by an embodiment of the present application in conjunction with the accompanying drawings of the specification. The specific implementation method may include (eg Figures 1 to 5 As shown in the figure): a hemisphere and a semi-ellipsoid are used to simulate craters and rocks respectively. After the model is generated, it is superimposed on the base plane; then a certain degree of Gaussian noise is superimposed on the generated plane, and then the sensor noise is analyzed and extracted, and a mask is performed on the obstacle image, which is further superimposed on the image to generate the final simulated image.

[0043] The solution provided in the embodiment of the present application includes the following steps:

[0044] (a1) Generate the base plane of superimposed obstacles

[0045] Furthermore, in a possible implementation, the point intervals of the point cloud obtained by laser scanning are not completely consistent, but for the convenience of data processing, the plane point cloud is arranged at equal intervals, the plane height Z is preset, and a basic plane point cloud parallel to the XOY plane is generated with the coordinates (0,0,Z) as the center.

[0046] (a2) Obstacle model establishment

[0047] Optionally, common types of obstacles during the landing phase of an extraterrestrial body include craters and rocks.

[0048] Meteorite craters can be divided into bowl-shaped "simple craters" with a diameter of less than a few kilometers, "complex craters" with a diameter of several kilometers or hundreds of kilometers, and some "multi-ring basins". In the process of landing obstacle avoidance, the height of obstacles accurately identified and avoided using laser point cloud data is about 100m, which is also the imaging height of the lidar. Taking a 30° field of view as an example, the size of the scene that can be seen is about 50m×50m. Therefore, at this height, the default rough obstacle avoidance has avoided larger complex craters and multi-ring basins, and there are only some simple craters. Therefore, a hemispherical model is used to simulate meteorite craters.

[0049] Rocks are another widely distributed topographic feature on the surface of celestial bodies. Their shapes are irregular, but by using some regular shapes and constructing surface roughness, rocks can be simulated more realistically. Commonly used models include semi-ellipsoids, hemispheres, tetrahedrons, etc. Here, the semi-ellipsoid is mainly used for analysis.

[0050] In a possible implementation, the steps of generating an obstacle database are as follows:

[0051] Investigate the size and density distribution of rocks and craters on the surface of celestial bodies, determine the depth-to-width ratio of craters, the ratio of rock height to bottom width, determine point intervals, and generate local point clouds.

[0052] A hemisphere is used to simulate a crater. The model equation is:

[0053] x 2 +y 2 +z 2 =R 2 (z≤0)

[0054] When generating a hemisphere, the center of the hemisphere interface is taken as the origin O, the cross section coincides with the XOY plane, and the crater mouth faces upward. The parametric equation of the corresponding hemisphere is determined by the depth-to-width ratio, and the depth of each point in the crater is determined under the condition of consistent point spacing.

[0055] Similarly, an ellipsoid is used to simulate rock. The model equation is:

[0056]

[0057] When generating rocks, assume that the cross section of the ellipsoid is a circle, with the center of the cross section as the origin O, the cross section coincides with the XOY plane, and the ellipsoid sphere faces upward. Determine the ellipsoid parameter equation of the corresponding rock by the depth-to-width ratio, determine the horizontal and vertical coordinates of each point in the rock by the above point interval, substitute them into the ellipsoid equation, and calculate the height.

[0058] The height and depth of the generated craters and rocks are stored according to their shapes (in the order determined by the horizontal and vertical coordinates).

[0059] (a3) Overlapping obstacles

[0060] In a possible implementation, the center point of the obstacle is determined on the generated base plane, the obstacle is selected from the obstacle database, the elevation values ​​are accumulated according to the corresponding positions of the XY coordinates, and a three-dimensional point cloud with the obstacle superimposed is generated.

[0061] (a4) Noise superposition

[0062] Furthermore, in a possible implementation, the point cloud data generated by the laser radar during the scanning process will inevitably contain noise due to various issues such as scene lighting, ground reflectivity, product laser energy, etc.

[0063] Common types of noise are:

[0064] 1) Ubiquitous small Gaussian noise;

[0065] 2) Point cloud data errors caused by insufficient return light at the edge of the field of view.

[0066] Optionally, in a possible implementation, for the first type of noise, Gaussian noise is generated according to the size of the base plane for superposition.

[0067] Optionally, in a possible implementation, for the second type of noise, firstly analyze the characteristics and distribution of this type of noise in the product, extract the location of the non-returned light area of ​​the image, perform mask processing on the obstacle image, and superimpose this type of noise.

[0068] The present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and when the computer instructions are executed on a computer, the computer executes Figure 1 The method described.

[0069] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) that contain computer-usable program code.

[0070] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0071] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0072] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0073] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

[0074] The contents not described in detail in the specification of the present invention belong to the common knowledge of those skilled in the art.

Claims

1. A method for modeling point cloud of obstacles on the surface of an extraterrestrial body, characterized in that: The steps include: Generate a basic plane model superimposed with obstacles on the surface of an extraterrestrial body; Determine the types of obstacles on the surface of extraterrestrial bodies and the models for simulating various obstacles, and generate an obstacle database; Determine the center point of the obstacle on the basic plane model, select the obstacle from the obstacle database, accumulate the elevation values ​​according to the corresponding positions of the XY coordinates, and generate a three-dimensional point cloud with the obstacle superimposed; Add a noise model that simulates the noise generated by the laser radar during the scanning process to the 3D point cloud to complete the point cloud modeling of obstacles on the surface of extraterrestrial bodies; The generation of the obstacle database comprises the following steps: Investigate the size and density distribution of rocks and craters on the surface of celestial bodies, determine the depth-to-width ratio of craters, the ratio of rock height to bottom width, determine point intervals, and generate local point clouds; Generate a hemispherical model: take the center of the hemispherical interface as the origin O, the cross section coincides with the XOY plane, and the crater mouth faces upward; determine the parametric equation of the corresponding hemisphere based on the depth-to-width ratio, and determine the depth of each point in the crater under the condition of consistent point spacing; Generate a semi-ellipsoid model: When generating rocks, assume that the cross section of the ellipsoid is a circle, with the center of the cross section as the origin O, the cross section coincides with the XOY plane, and the ellipsoid is facing upwards; determine the ellipsoid parameter equation of the corresponding rock by the depth-to-width ratio, determine the horizontal and vertical coordinates of each point in the rock by the above point interval, substitute them into the ellipsoid equation, and calculate the height; The height and depth of generated crater and rock models are stored in their shapes.

2. The method for modeling an extraterrestrial body surface obstacle point cloud according to claim 1, characterized in that: The generation of a basic plane model for superimposing obstacles on the surface of an extraterrestrial body comprises the following steps: arranging the plane point clouds at equal intervals, presetting the plane height Z, and generating a basic plane point cloud with the coordinates (0,0,Z) as the center.

3. The method for modeling an extraterrestrial body surface obstacle point cloud according to claim 2, characterized in that: The generated basic plane is a plane with a certain slope, and the data format is a normalized three-dimensional point cloud.

4. The method for modeling an extraterrestrial body surface obstacle point cloud according to claim 1, characterized in that: The types of obstacles on the surface of the extraterrestrial body include craters and rocks, which are simulated using a hemispherical model and a semi-ellipsoidal body respectively.

5. The method for modeling an extraterrestrial body surface obstacle point cloud according to claim 1, characterized in that: The noise model includes Gaussian white noise and point cloud data error noise caused by insufficient return light at the edge of the field of view.

6. A point cloud modeling system for extraterrestrial surface obstacles, characterized in that: include: The first module is used to generate a basic plane model superimposed with obstacles on the surface of an extraterrestrial body; The second module is used to determine the types of obstacles on the surface of extraterrestrial bodies and the models for simulating various obstacles, and to generate an obstacle database; The third module is used to determine the center point of the obstacle on the basic plane model, select the obstacle from the obstacle database, accumulate the elevation value according to the corresponding position of the XY coordinates, and generate a three-dimensional point cloud after superimposing the obstacle; The fourth module is used to add a noise model to the 3D point cloud to simulate the noise generated by the laser radar during the scanning process; The generation of the obstacle database comprises the following steps: Investigate the size and density distribution of rocks and craters on the surface of celestial bodies, determine the depth-to-width ratio of craters, the ratio of rock height to bottom width, determine point intervals, and generate local point clouds; Generate a hemispherical model: take the center of the hemispherical interface as the origin O, the cross section coincides with the XOY plane, and the crater mouth faces upward; determine the parametric equation of the corresponding hemisphere based on the depth-to-width ratio, and determine the depth of each point in the crater under the condition of consistent point spacing; Generate a semi-ellipsoid model: When generating rocks, assume that the cross section of the ellipsoid is a circle, with the center of the cross section as the origin O, the cross section coincides with the XOY plane, and the ellipsoid is facing upwards; determine the ellipsoid parameter equation of the corresponding rock by the depth-to-width ratio, determine the horizontal and vertical coordinates of each point in the rock by the above point interval, substitute them into the ellipsoid equation, and calculate the height; The height and depth of generated crater and rock models are stored in their shapes.

7. The extraterrestrial body surface obstacle point cloud modeling system according to claim 6, characterized in that: The generating of the basic plane model superimposed with the extraterrestrial body surface obstacle comprises the following steps: arranging the plane point cloud at equal intervals, presetting the plane height Z, and generating the basic plane point cloud with the coordinate (0,0,Z) as the center; The generated basic plane is a plane with a certain slope, and the data format is a normalized three-dimensional point cloud; The types of obstacles on the surface of the extraterrestrial body include craters and rocks, which are simulated using a hemispherical model and a semi-ellipsoidal model respectively; The generation of the obstacle database comprises the following steps: Investigate the size and density distribution of rocks and craters on the surface of celestial bodies, determine the depth-to-width ratio of craters, the ratio of rock height to bottom width, determine point intervals, and generate local point clouds; Generate a hemispherical model: take the center of the hemispherical interface as the origin O, the cross section coincides with the XOY plane, and the crater mouth faces upward; determine the parametric equation of the corresponding hemisphere based on the depth-to-width ratio, and determine the depth of each point in the crater under the condition of consistent point spacing; Generate a semi-ellipsoid model: When generating rocks, assume that the cross section of the ellipsoid is a circle, with the center of the cross section as the origin O, the cross section coincides with the XOY plane, and the ellipsoid is facing upwards; determine the ellipsoid parameter equation of the corresponding rock by the depth-to-width ratio, determine the horizontal and vertical coordinates of each point in the rock by the above point interval, substitute them into the ellipsoid equation, and calculate the height; Store the height and depth of the generated crater and rock models according to their shapes; The noise model includes Gaussian white noise and point cloud data error noise caused by insufficient return light at the edge of the field of view.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

9. An extraterrestrial surface obstacle point cloud modeling device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

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