Method and device for generating digital terrain model data, electronic equipment and storage medium

Abstract

The present disclosure relates to a method, device, electronic equipment and storage medium for digital terrain model data, the method comprising: obtaining digital elevation model data in a target operation area; generating the digital elevation model data into target digital elevation model data with multiple resolutions; obtaining target terrain factors of the target digital elevation model data at different resolutions; and obtaining digital terrain model data with multiple resolutions based on the target digital elevation model data and the target terrain factors. Since the embodiments of the present disclosure can generate digital terrain model data containing target terrain factors and having multiple resolutions, the safety and efficiency of driving can be greatly improved when an unmanned vehicle operates in a target operation area based on a map containing the digital terrain model data.

Description

Technical Field

[0001] This disclosure relates to the field of terrain technology, and in particular to methods, apparatus, electronic devices and storage media for generating digital terrain model data. Background Technology

[0002] As autonomous driving technology continues to develop in mining environments, the demand for high-precision maps in these environments is also increasing. High-precision, high-density point cloud data of terrain-changing areas acquired through vehicle-mounted LiDAR can generate high-resolution DEMs (Digital Elevation Models) for high-precision, high-frequency updates of vector maps. However, such high-precision maps often fail to meet the needs of autonomous driving in mining scenarios. Summary of the Invention

[0003] This disclosure provides a method, apparatus, electronic device, and storage medium for generating digital terrain model data.

[0004] According to a first aspect of this disclosure, a method for generating digital terrain model data is provided, the method comprising:

[0005] Obtain digital elevation model data for the target work area;

[0006] The digital elevation model data is used to generate target digital elevation model data with multiple resolutions.

[0007] Obtain the target terrain factors of the target digital elevation model data at different resolutions;

[0008] Based on the target digital elevation model data and the target terrain factor, digital terrain model data with multi-resolution is obtained.

[0009] According to a second aspect of this disclosure, an apparatus for generating digital terrain model data is provided, characterized in that the apparatus comprises:

[0010] The data acquisition module is used to acquire digital elevation model data for the target work area;

[0011] The data processing module is used to generate target digital elevation model data with multiple resolutions from the digital elevation model data.

[0012] The terrain factor acquisition module is used to acquire the target terrain factors of the target digital elevation model data at different resolutions;

[0013] The digital terrain model data generation module is used to obtain multi-resolution digital terrain model data based on the target digital elevation model data and the target terrain factors.

[0014] According to a third aspect of this disclosure, an electronic device is provided. The electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the method described above.

[0015] According to a fourth aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the methods described above.

[0016] The digital terrain model data generation method, apparatus, electronic device, and storage medium provided in this disclosure involve: acquiring digital elevation model data in a target work area; generating target digital elevation model data with multiple resolutions from the digital elevation model data; acquiring target terrain factors of the target digital elevation model data at different resolutions; and obtaining multi-resolution digital terrain model data based on the target digital elevation model data and the target terrain factors. Because this disclosure can generate multi-resolution digital terrain model data containing target terrain factors, it significantly improves the safety and efficiency of unmanned vehicles operating in a target work area based on a map containing this digital terrain model data. Attached Figure Description

[0017] Further details, features, and advantages of this disclosure are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which:

[0018] Figure 1 A schematic diagram of a segmented multi-resolution DEM provided as an exemplary embodiment of this disclosure;

[0019] Figure 2 A schematic diagram of a slice provided for an exemplary embodiment of this disclosure;

[0020] Figure 3 A flowchart illustrating a method for generating digital terrain model data provided as an exemplary embodiment of this disclosure;

[0021] Figure 4 A schematic block diagram of the functional modules of a digital terrain model data generation apparatus provided in an exemplary embodiment of the present disclosure;

[0022] Figure 5 A structural block diagram of an electronic device provided as an exemplary embodiment of this disclosure;

[0023] Figure 6 A block diagram of a computer system provided for an exemplary embodiment of this disclosure. Detailed Implementation

[0024] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0025] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0026] The term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below. It should be noted that the concepts of "first", "second", etc., used in this disclosure are only used to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies.

[0027] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0028] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0029] With the in-depth research and implementation of autonomous driving technology in mining scenarios, the demand for high-precision maps in mining areas is increasing. This requires not only centimeter-level accuracy in vector maps but also rapid updates to address the frequent terrain changes caused by mining operations. By acquiring high-precision, high-density point cloud data of terrain-changing areas using vehicle-mounted LiDAR, high-resolution DEMs of open-pit mines can be generated for high-precision, high-frequency updates of vector maps.

[0030] However, the terrain in the mining area is complex, mainly consisting of unstructured roads, and the terrain factors such as road slope, aspect, and bumpiness vary greatly in different areas. To ensure the safe driving of autonomous vehicles, the influence of terrain factors must be considered in the early global path planning, local path planning, and real-time perception decision-making. Furthermore, a reasonable driving trajectory should be planned based on the DTM (Digital Terrain Model) of the route area, and a reasonable speed limit should be set to balance efficiency and safety.

[0031] Therefore, in this embodiment of the disclosure, by generating a multi-resolution DTM, not only can the terrain factors in each region be fully considered, but also by generating DTMs with different resolutions, different autonomous vehicles can select the corresponding resolution DTM according to their own vehicle parameters, which can improve the operating efficiency of autonomous vehicles when operating in mining scenarios.

[0032] In the embodiments provided in this disclosure, high-resolution DEM data is first obtained based on laser point cloud data.

[0033] For example, DEM data can be obtained using LiDAR (Light Detection and Ranging) on ​​vehicles, airborne equipment, or other vehicles. This allows for the low-cost and high-efficiency acquisition of high-density, high-precision point cloud data of the target area. The acquired point cloud data can be used to generate a DEM. A DEM uses limited terrain elevations to digitally simulate or represent the terrain surface morphology, i.e., by rasterizing the terrain surface and recording the elevation value of each grid point to characterize terrain features. Digital elevation models can provide basic terrain data support for high-precision maps, terrain analysis, decision planning, and other applications in autonomous driving scenarios. The high-resolution DEM obtained in the embodiment may, for example, have a resolution of 0.1m.

[0034] For autonomous vehicles in mining scenarios, since different types or models of autonomous vehicles vary in size, they can be categorized. For example, vehicles can be divided into multiple categories based on their size. Then, a corresponding resolution DTM (Depth Measurement Model) can be generated for each category of autonomous vehicle, allowing them to navigate and complete tasks based on the appropriate resolution DTM during operation in the mining scenario. In this embodiment, the mining scenario can be an open-pit mine, etc.

[0035] In this embodiment, a reasonable DTM multi-resolution index can be designed. Open-pit mine autonomous driving systems generally include various types of vehicles, including wide-body trucks, large mining trucks, mining vehicles, and forklifts. Since different vehicle models have different parameters, especially wheel size and body size, to ensure the safety of autonomous driving, different resolution DTMs are needed for global and local path planning based on different vehicle parameters. For example, vehicles with smaller wheels or bodies use a higher resolution DTM, while vehicles with larger wheels or bodies use a lower resolution DTM. This can improve the operational efficiency of the autonomous vehicle during operation. In this embodiment, three resolution DTMs can be designed, for example, 0.4 meters, 2.5 meters, and 5.0 meters respectively.

[0036] Since DTMs of different resolutions require DEMs of different resolutions to generate, the generation methods for multi-resolution DEMs can be combined with... Figure 1 To illustrate. See also Figure 1 As shown, Figure 1 This is a schematic diagram illustrating the division of a multi-resolution DEM in an embodiment of this disclosure. The outer large square represents a 100x100 column DEM with a resolution of 0.1m. Within this data range, equidistant segmentation and statistical calculations are performed. Specifically, R5 is a 2x2 column DEM with a resolution of 5.0m, R2.5 is a 4x4 column DEM with a resolution of 2.5m, and R0.4 is a 25x25 column DEM with a resolution of 0.4m.

[0037] The embodiments of this disclosure can process the generated high-precision DEM into multi-resolution DEM data in the manner described above, so as to generate a corresponding multi-resolution DTM, so that autonomous vehicles can adopt a DTM of the corresponding resolution according to their own vehicle type and other information.

[0038] Because the terrain in mining scenarios is complex, typically consisting of unstructured roads, and terrain factors such as road slope, aspect, and bumpiness vary greatly across different areas, the impact of terrain factors must be considered during the initial global path planning, local path planning, and real-time perception decision-making processes to ensure the safe operation of autonomous vehicles. Therefore, in the process of generating a DTM based on a DEM, this embodiment of the present disclosure needs to consider terrain factors such as road slope, aspect, and bumpiness to ensure the safety of the autonomous vehicle during operation.

[0039] Among them, the terrain slope characterizes the steepness of the surface unit and is expressed as the ratio of the vertical height of the slope unit to the horizontal distance. In mining areas, the slope height of 100 meters on the plane is commonly used to represent it. The slope aspect characterizes the projection direction of the slope normal onto the horizontal plane (from high to low) and is expressed as 0 to 360 degrees north of east. Flat slopes have no direction and are fixed at -1.

[0040] In this embodiment of the disclosure, the slope and aspect of the DTM can be generated based on a high-resolution DEM, and the obtained slope and aspect can be used as the slope and aspect of a low-resolution DTM. In the embodiment, the slope and aspect of multiple resolution DTMs can also be generated based on multiple resolution DEMs.

[0041] In this embodiment, under a DEM of the corresponding resolution, all elevation points within the radius of the grid points on the slope can be selected. These elevation points can be fitted to a plane using the least squares method, and the normal vector of this plane can be used as the normal vector of the slope. The slope's normal vector can then be converted into the corresponding slope and aspect. The grid points on the slope can be selected from all grid points included in the slope, or a subset of grid points can be selected, such as grid points near the center of the slope.

[0042] Furthermore, in the embodiments provided in this disclosure, the bumpiness of the road surface topography factor in the mining area characterizes the road surface undulation and erosion degree, and can be represented by the ratio of the total area of ​​the grid surface to the projected area. Specifically, a regularized triangular mesh can be first formed on the high-resolution DEM, and the area of ​​each small triangle can be calculated. Thus, based on the DEM at each resolution, the sum of the areas of the small triangles in each grid is first counted, and then the projected area of ​​the grid is calculated. The ratio of the two is the bumpiness of the grid at that resolution. The projected area can be obtained by first obtaining the normal vector of the grid and then projecting the grid along its normal vector direction. There can be multiple grids, and the total area of ​​the grid surface is the sum of the areas of each surface of the multiple grids, while the projected area is the sum of the projected areas of each grid projected onto its opposite side based on the normal vector direction. By comparing the surface area of ​​the grid on the road surface with the projected area, the smoothness of the road surface, i.e., the bumpiness, can be reflected. Using bumpiness as a topography factor can effectively measure the quality of the road.

[0043] In the embodiments provided in this disclosure, the multi-resolution DTM calculated above can be uniformly mapped to the same row and column organization structure based on the slicing method of DEM data. Based on this unified slicing organization rule, the DTM is only updated for local areas where the terrain changes in the business, which can realize incremental updates of the terrain data of the target area and effectively improve production efficiency.

[0044] In the embodiments, unified tile organization rules and data structures for DEM data can be set. For example... Figure 2As shown, the planar position range can be pre-divided into multiple slices, with different slices corresponding to different planar position ranges (including different planar x-coordinate ranges and / or different planar y-coordinate ranges). For example, the planar x-coordinate range of a slice is [x1, x2], and the planar y-coordinate range is [y1, y2]; the planar x-coordinate range of an adjacent slice in the same row is [x2, x3], and the planar y-coordinate range is [y1, y2]; the planar x-coordinate range of an adjacent slice in the same column is [x1, x2], and the planar y-coordinate range is [y2, y3], where x1, x2, and x3 are distinct real numbers, and y1, y2, and y3 are also distinct real numbers.

[0045] The dimensions of each slice can be the same or different. For ease of explanation, the following example assumes that all slices are the same size. The row side length RL and column side length CL of the slices can be set based on data management efficiency and flexibility. The row side length RL and column side length CL can be the same or different. Optionally, both the row side length RL and column side length CL can be set to 10 meters.

[0046] In some embodiments, a fixed reference point B0 can be set based on the area of ​​the target work area, such as... Figure 2 As shown, the coordinates of the reference point are (-10000, 10000). After determining the reference point, the slice with the reference point B0 as its top-left corner can be used as the slice in row 0 and column 0, thus obtaining the row and column numbers of each slice. Here, m represents the row number of the slice and n represents the column number of the slice.

[0047] Each slice can be identified using its row and column coordinates. For example, {0,0} can be used to identify the slice in row 0, column 0, and {0,1} can be used to identify the slice in row 0, column 1, and so on. Alternatively, slices can be identified by their numbers. For example, slices can be numbered row by row. Assuming each row contains u slices, the slices in the first row can be numbered 0, 1, 2, ..., u-1; the slices in the second row can be numbered u, u+1, u+2, ..., 2u-1; and so on.

[0048] It is understood that the above slice organization rules are merely illustrative. For example, in addition to the reference point shown in the figure, other coordinate positions can also be used as reference point B0. As another example, a slice with reference point B0 as its lower left, lower right, or upper right corner can be used as the slice in row 0 and column 0, thus obtaining the row and column numbers of each slice. Appropriate slice organization rules and data structures can be selected according to actual circumstances, and the embodiments disclosed herein are not limited thereto.

[0049] Based on the above embodiments, in the embodiments provided in this disclosure, such as Figure 3 As shown, a method for generating digital terrain model data is provided, which may include the following steps:

[0050] In step S310, digital elevation model data of the target work area is obtained.

[0051] In this embodiment of the disclosure, in a target work area within a mining scenario, an autonomous vehicle can travel along a pre-planned path between loading and unloading points during operation, transporting minerals from the loading point to the unloading point. The autonomous vehicle is equipped with a LiDAR (Light Detection and Ranging) sensor, which can collect point cloud data of the target work area during its movement. This point cloud data can include the three-dimensional coordinates of any point within the LiDAR's sensing range, including but not limited to ground points along the path, points on trees in the target work area, points on pedestrians in the target work area, and points on mineral piles at the loading point. A processing unit can be deployed on the autonomous vehicle to generate DEM (Digital Elevation Model) data of the target work area based on the point cloud data collected by the LiDAR. Alternatively, the autonomous vehicle can send the point cloud data collected by the LiDAR to the cloud, allowing the cloud to generate DEM data of the target work area based on the received point cloud data.

[0052] In step S320, the digital elevation model data is used to generate target digital elevation model data with multiple resolutions.

[0053] In this embodiment, DEM data with multiple resolutions can be generated as needed. See details below. Figure 1 The corresponding embodiments described above will not be repeated here.

[0054] In step S330, the target terrain factors of the target digital elevation model data at different resolutions are obtained.

[0055] In step S340, digital terrain model data with multiple resolutions is obtained based on the target digital elevation model data and the target terrain factors.

[0056] Due to the complex terrain of the mining area, which is mainly composed of unstructured roads, and the significant variations in terrain factors such as road slope, aspect, and bumpiness across different regions, terrain factors need to be considered to ensure the safe operation of autonomous vehicles. Furthermore, since different vehicle models can select DTM data at corresponding resolutions, it is necessary to obtain the target terrain factors of the target digital elevation model data at different resolutions in order to generate DTM data with multiple resolutions.

[0057] The method for generating digital terrain model data provided in this disclosure involves: acquiring digital elevation model data in a target work area; generating target digital elevation model data with multiple resolutions from the digital elevation model data; acquiring target terrain factors at different resolutions from the target digital elevation model data; and obtaining multi-resolution digital terrain model data based on the target digital elevation model data and the target terrain factors. Because this disclosure can generate multi-resolution digital terrain model data containing target terrain factors, it significantly improves the safety and efficiency of unmanned vehicles operating in a target work area based on a map containing this digital terrain model data.

[0058] In the embodiments provided in this disclosure, the target terrain factor may include slope and aspect. When obtaining the target terrain factor at the target resolution, the slope surface of the target digital elevation model data at the target resolution can be acquired, and the elevation information contained in the slope surface within the target range can be determined. The normal vector of the slope surface is determined through the elevation information, and the slope and aspect of the slope surface are determined based on the normal vector. When determining the normal vector of the slope surface through the elevation information, grid points in the slope surface at the target resolution can be identified, and multiple elevation points contained in the grid points can be obtained based on the elevation information. The multiple elevation points are fitted to a target plane based on a target fitting algorithm, and the normal vector of the fitted plane is used as the normal vector of the slope surface.

[0059] In this embodiment, under a DEM of the corresponding resolution, all elevation points within the radius of the grid points on the slope can be selected. These elevation points can be fitted to a plane using the least squares method, and the normal vector of this plane can be used as the normal vector of the slope. The slope's normal vector can then be converted into the corresponding slope and aspect. The grid points on the slope can be selected from all grid points included in the slope, or a subset of grid points can be selected, such as grid points near the center of the slope.

[0060] In the embodiments provided in this disclosure, the target terrain factor also includes bumpiness, which can be determined by acquiring the road surface from the target digital elevation model data and obtaining the surface area of ​​the grid facets of the road surface. The bumpiness of the road surface is determined by determining the projected area of ​​the grid facets and based on the surface area and projected area. In this embodiment, the road surface can be a section of the road surface along a travel route.

[0061] Specifically, in the embodiments provided in this disclosure, the bumpiness of the road surface topography factor in the mining area characterizes the road surface undulation and erosion degree, and can be represented by the ratio of the total area of ​​the grid surface to the projected area. Specifically, a regularized triangular mesh can be first created on the high-resolution DEM, and the area of ​​each small triangle can be calculated. Based on the DEM at its respective resolution, the sum of the areas of the small triangles within each grid is first calculated, and then the projected area of ​​the grid is calculated. The ratio of the two is the bumpiness of the grid at that resolution. The projected area can be obtained by first acquiring the normal vector of the grid and then projecting the grid along its normal vector direction. There can be multiple grids, and the total area of ​​the grid surface is the sum of the areas of each surface of the multiple grids. The projected area is the sum of the projected areas of each grid projected onto its corresponding normal vector direction. By comparing the surface area of ​​the grid on the road surface with the projected area, the smoothness of the road surface, i.e., the bumpiness, can be reflected. Using bumpiness as a topography factor can effectively measure the quality of the road.

[0062] In the embodiments provided in this disclosure, the DEM can be first divided into multiple slices. Upon detecting an update to the digital elevation model data, the updated slice is determined; the digital terrain model data is then updated based on the updated slice. This method of producing updated DEMs only for localized areas where terrain changes allows for incremental updates of the digital terrain model data for the target area, effectively improving production efficiency.

[0063] In this embodiment, since unmanned vehicles in mining scenarios generally include various types of vehicles, such as wide-body vehicles, large mining trucks, mining vehicles, and forklifts, and because different vehicle models have different parameters, especially wheel size and vehicle size, different resolution Digital Terrain Models (DTMs) are needed for global and local path planning to ensure the safety of autonomous driving. Therefore, the vehicle model parameters of the target unmanned vehicle can be obtained, and corresponding target resolution digital terrain model data can be obtained based on the vehicle type. The target unmanned vehicle can then be controlled to operate in the target work area based on the target resolution digital terrain model data. For example, vehicles with smaller wheels or bodies use a higher resolution DTM, while vehicles with larger wheels or bodies use a lower resolution DTM. Furthermore, the target driving path of the target unmanned vehicle in the target work area can be determined using the target resolution digital terrain model data, and the target unmanned vehicle can be controlled to drive in the target work area based on the target driving path. This can improve the operational efficiency of the unmanned vehicle during operation.

[0064] By dividing each functional module according to its corresponding function, this disclosure provides a digital terrain model data generation device, which can be a server or a chip applied to a server. Figure 4A schematic block diagram of the functional modules of a digital terrain model data generation apparatus provided as an exemplary embodiment of this disclosure. Figure 4 As shown, the apparatus for generating digital terrain model data includes:

[0065] Data acquisition module 10 is used to acquire digital elevation model data in the target work area;

[0066] Data processing module 20 is used to generate target digital elevation model data with multi-resolution from the digital elevation model data;

[0067] The terrain factor acquisition module 30 is used to acquire the target terrain factors of the target digital elevation model data at different resolutions.

[0068] The digital terrain model data generation module 40 is used to obtain multi-resolution digital terrain model data based on the target digital elevation model data and the target terrain factors.

[0069] The digital terrain model data generation apparatus provided in this disclosure acquires digital elevation model data in a target work area; generates target digital elevation model data with multiple resolutions from the digital elevation model data; acquires target terrain factors at different resolutions from the target digital elevation model data; and obtains multi-resolution digital terrain model data based on the target digital elevation model data and the target terrain factors. Because this disclosure can generate multi-resolution digital terrain model data containing target terrain factors, it greatly improves the safety and efficiency of unmanned vehicles operating in a target work area based on a map containing this digital terrain model data.

[0070] In another embodiment provided in this disclosure, the target terrain factors include slope and aspect; the device further includes:

[0071] The slope acquisition module is used to acquire the slope of the target digital elevation model data at the target resolution;

[0072] The elevation information determination module is used to determine the elevation information contained within the target area of ​​the slope.

[0073] The data determination module is used to determine the normal vector of the slope surface through the elevation information, and to determine the slope and aspect of the slope surface based on the normal vector.

[0074] In another embodiment provided in this disclosure, the data determination module is further configured to:

[0075] Determine the grid points in the slope at the target resolution;

[0076] Based on the elevation information, obtain multiple elevation points contained in the grid points;

[0077] The multiple elevation points are fitted to a target plane using a target fitting algorithm, and the normal vector of the fitted plane is used as the normal vector of the slope.

[0078] In another embodiment provided in this disclosure, the target terrain factor includes bumpiness; the device further includes:

[0079] The road surface acquisition module is used to acquire the road surface in the target digital elevation model data;

[0080] The surface area acquisition module is used to acquire the surface area of ​​the middle grid surface of the road surface;

[0081] A projection area determination module is used to determine the projection area of ​​the grid surface;

[0082] The bumpiness determination module is used to determine the bumpiness of the road surface based on the surface area and the projected area.

[0083] In another embodiment provided in this disclosure, the digital elevation model data is first divided into multiple slices, and the apparatus further includes:

[0084] The slice update determination module is used to determine the updated slice in which the digital elevation model data has been updated when an update is detected in the digital elevation model data.

[0085] The data update module is used to update the digital terrain model data based on the update slice.

[0086] In yet another embodiment provided in this disclosure, the apparatus further includes:

[0087] The parameter acquisition module is used to acquire the vehicle model parameters of the target autonomous vehicle.

[0088] The terrain data acquisition module is used to acquire digital terrain model data with a corresponding target resolution based on the vehicle type.

[0089] The control module is used to control the target unmanned vehicle to operate in the target work area based on the digital terrain model data of the target resolution.

[0090] In yet another embodiment provided in this disclosure, the apparatus further includes:

[0091] The driving path determination module is used to determine the target driving path of the target unmanned vehicle in the target work area using the digital terrain model data of the target resolution;

[0092] A driving module is used to control the target unmanned vehicle to drive in the target work area based at least on the target driving path.

[0093] Since the device embodiments correspond to the method embodiments described above, please refer to the description of the method embodiments for details, which will not be repeated here.

[0094] This disclosure also provides an electronic device, including: at least one processor; a memory for storing processor-executable instructions; wherein the at least one processor is configured to execute the instructions to implement the methods disclosed in this disclosure.

[0095] Figure 5 This is a schematic diagram of the structure of an electronic device provided as an exemplary embodiment of this disclosure. For example... Figure 5 As shown, the electronic device 1800 includes at least one processor 1801 and a memory 1802 coupled to the processor 1801. The processor 1801 can perform the corresponding steps in the methods disclosed in the embodiments of this disclosure.

[0096] The processor 1801 described above can also be called a central processing unit (CPU), which can be an integrated circuit chip with signal processing capabilities. Each step in the method disclosed in this embodiment can be implemented by the integrated logic circuitry in the processor 1801 or by software instructions. The processor 1801 can be a general-purpose processor, a digital signal processor (DSP), an ASIC, a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this embodiment can be directly implemented by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software modules can be located in the memory 1802, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The processor 1801 reads information from the memory 1802 and, in conjunction with its hardware, completes the steps of the method described above.

[0097] Furthermore, various operations / processes according to this disclosure, implemented via software and / or firmware, can be transmitted from a storage medium or network to a computer system with a dedicated hardware architecture, such as... Figure 6The computer system 1900 shown is equipped with the programs that constitute the software. When various programs are installed, the computer system is able to perform various functions, including those described above. Figure 6 A block diagram of a computer system provided for an exemplary embodiment of this disclosure.

[0098] Computer System 1900 is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0099] like Figure 6 As shown, the computer system 1900 includes a computing unit 1901, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 1902 or a computer program loaded from a storage unit 1908 into a random access memory (RAM) 1903. The RAM 1903 may also store various programs and data required for the operation of the computer system 1900. The computing unit 1901, ROM 1902, and RAM 1903 are interconnected via a bus 1904. An input / output (I / O) interface 1905 is also connected to the bus 1904.

[0100] Multiple components in computer system 1900 are connected to I / O interface 1905, including: input unit 1906, output unit 1907, storage unit 1908, and communication unit 1909. Input unit 1906 can be any type of device capable of inputting information into computer system 1900. Input unit 1906 can receive input digital or character information and generate key signal inputs related to user settings and / or function control of the electronic device. Output unit 1907 can be any type of device capable of presenting information and may include, but is not limited to, a monitor, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 1908 may include, but is not limited to, hard disks and optical disks. Communication unit 1909 allows computer system 1900 to exchange information / data with other devices via a network such as the Internet, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers, and / or chipsets, such as Bluetooth™ devices, WiFi devices, WiMax devices, cellular communication devices, and / or the like.

[0101] The computing unit 1901 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 1901 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 1901 performs the various methods and processes described above. For example, in some embodiments, the methods disclosed in this disclosure can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 1908. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 1900 via ROM 1902 and / or communication unit 1909. In some embodiments, the computing unit 1901 can be configured to perform the methods disclosed in this disclosure by any other suitable means (e.g., by means of firmware).

[0102] This disclosure also provides a computer-readable storage medium, wherein when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is able to perform the methods disclosed in this disclosure.

[0103] The computer-readable storage medium in this disclosure can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. The aforementioned computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specifically, the aforementioned computer-readable storage medium may include electrical connections based on one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0104] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0105] This disclosure also provides a computer program product, including a computer program, wherein when the computer program is executed by a processor, it implements the methods disclosed in the embodiments of this disclosure.

[0106] In embodiments of this disclosure, computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include, but are not limited to, object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network (including a local area network (LAN) or a wide area network (WAN)), or it can be connected to an external computer.

[0107] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0108] The modules, components, or units described in the embodiments of this disclosure can be implemented in software or hardware. The names of the modules, components, or units do not necessarily constitute a limitation on the module, component, or unit itself.

[0109] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary hardware logic components that can be used include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0110] The above description is merely an embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features disclosed in this disclosure that have similar functions.

[0111] While specific embodiments of this disclosure have been described in detail by way of example, those skilled in the art should understand that the examples are for illustrative purposes only and not intended to limit the scope of this disclosure. Those skilled in the art should understand that modifications can be made to the above embodiments without departing from the scope and spirit of this disclosure. The scope of this disclosure is defined by the appended claims.

Claims

1. A method for generating digital terrain model data, characterized in that, The method includes: Obtain digital elevation model data for the target work area; The digital elevation model data is used to generate target digital elevation model data with multiple resolutions. Obtain the target terrain factors of the target digital elevation model data at different resolutions; Based on the target digital elevation model data and the target terrain factor, obtain digital terrain model data with multi-resolution; The method further includes: Obtain the vehicle model parameters of the target autonomous vehicle; Based on the vehicle model parameters, obtain digital terrain model data with the corresponding target resolution; The target unmanned vehicle is controlled to operate in the target work area based on the digital terrain model data of the target resolution. The target terrain factors include slope and aspect; the method further includes: Obtain the slope surface of the target digital elevation model data at the target resolution; Determine the elevation information contained within the target area of ​​the slope; Determine the grid points in the slope at the target resolution; Based on the elevation information, obtain multiple elevation points contained in the grid points; The multiple elevation points are fitted to a target plane using a target fitting algorithm, and the normal vector of the target plane is used as the normal vector of the slope. The slope and aspect of the slope are then determined based on the normal vector.

2. The method according to claim 1, characterized in that, The target terrain factor includes bumpiness; the method further includes: Obtain the road surface from the target digital elevation model data; Obtain the surface area of ​​the middle grid surface of the road surface; Determine the projected area of ​​the grid surface; The bumpiness of the road surface is determined based on the surface area and the projected area.

3. The method according to claim 1, characterized in that, The digital elevation model data is first divided into multiple slices, and the method further includes: If an update to the digital elevation model data is detected, the updated slice in which the digital elevation model data has been updated is determined; The digital terrain model data is updated based on the updated slice.

4. The method according to claim 3, characterized in that, The method further includes: The target driving path of the target unmanned vehicle in the target work area is determined by the digital terrain model data of the target resolution. The target unmanned vehicle is controlled to travel in the target work area based at least on the target driving path.

5. A device for generating digital terrain model data, characterized in that, The device includes: The data acquisition module is used to acquire digital elevation model data for the target work area; The data processing module is used to generate target digital elevation model data with multiple resolutions from the digital elevation model data. The terrain factor acquisition module is used to acquire the target terrain factors of the target digital elevation model data at different resolutions; A digital terrain model data generation module is used to obtain multi-resolution digital terrain model data based on the target digital elevation model data and the target terrain factors; the target terrain factors include slope and aspect. The parameter acquisition module is used to acquire the vehicle model parameters of the target autonomous vehicle. The terrain data acquisition module is used to acquire digital terrain model data with a corresponding target resolution based on the vehicle parameters. The control module is used to control the target unmanned vehicle to operate in the target work area based on the digital terrain model data of the target resolution; The determination module is used to acquire the slope surface of the target digital elevation model data at a target resolution; determine the elevation information contained in the slope surface within the target range; determine the grid points in the slope surface at the target resolution; acquire multiple elevation points contained in the grid points based on the elevation information; fit the multiple elevation points into a target plane based on a target fitting algorithm, and use the normal vector of the target plane as the normal vector of the slope surface, and determine the slope and aspect of the slope surface based on the normal vector.

6. An electronic device, characterized in that, include: At least one processor; Memory for storing the at least one processor-executable instruction; The at least one processor is configured to execute the instructions to implement the method as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, When the instructions in the computer-readable storage medium are executed by the processor of the electronic device, the electronic device is able to perform the method as described in any one of claims 1-4.

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