Underground poor texture space high-precision mapping method based on mobile-solid collaborative correction

Abstract

The invention provides an underground poor texture space high-precision mapping method based on mobile-solid collaborative correction, and relates to the technical field of map construction, and the method comprises the steps: obtaining sensor data of sensor nodes determined based on an underground poor texture space structure; calculating initial position data of the unmanned aerial vehicle; sensor data are received through the unmanned aerial vehicle, and unmanned aerial vehicle position data are generated in combination with unmanned aerial vehicle initial position data and unmanned aerial vehicle laser radar detection data; correcting the position data of the unmanned aerial vehicle through a sectional mobile-fixed cooperative speedometer; obtaining point cloud data used for describing the underground poor texture space structure; and performing data fusion on the sensor data, the initial position data of the unmanned aerial vehicle, the corrected position data of the unmanned aerial vehicle and the point cloud data to obtain fused data, and establishing a high-precision map of the underground poor texture space by taking each sensor node as a feature point in a mapping process. According to the method, the high-precision diagram of the underground poor texture space can be accurately established, and the underground space management and application efficiency is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of map construction, and in particular to a high-precision underground poor texture space mapping method based on mobile-solid collaborative deviation correction. Background Art

[0002] High-precision mapping of underground texture-poor spaces is a method for precise positioning and map construction of underground scenes. Underground texture-poor spaces are areas in underground environments that lack rich visual features (i.e., texture information), such as tunnels, mines, or subway stations. Due to factors such as insufficient lighting, monotonous colors, and similar surface structures, these spaces usually lack feature points that can be visually identified, making image-based positioning and mapping difficult. The high-precision mapping method of underground texture-poor spaces can achieve high-precision mapping in low-texture underground spaces.

[0003] High-precision mapping of underground poor texture space based on collaborative correction of fixed-mobile and mobile systems is of great significance in modern urban planning, infrastructure construction, environmental protection, mining, disaster relief, archaeological research and the development of smart cities. Especially in the development of smart cities, high-precision underground maps are crucial for smart transportation and smart facility management. The construction and maintenance of some urban infrastructure require detailed underground pipeline networks and facility maps to reduce construction errors and accident risks.

[0004] However, in underground texture-poor spaces, due to insufficient lighting and single texture, it is difficult for visual sensors to extract enough feature points, resulting in low positioning and mapping accuracy. Traditional simultaneous positioning and mapping technologies commonly used in underground environments have poor data stability in narrow spaces or complex terrain conditions, and cannot obtain continuous and accurate map data. Due to the narrow and long underground tunnels, loop detection is difficult to achieve and odometer errors are difficult to correct, resulting in the gradual accumulation of positioning errors, which in turn affects map accuracy. Summary of the invention

[0005] In order to solve the technical problems existing in the prior art that in underground texture-poor spaces, due to insufficient light and single texture, it is difficult for visual sensors to extract enough feature points, resulting in low positioning and mapping accuracy, and the sensors commonly used in traditional simultaneous positioning and mapping technologies in underground environments have poor data stability in narrow spaces or complex terrain conditions, and cannot obtain continuous and accurate map data. Due to the narrow and long underground tunnels, loop detection is difficult to achieve and odometer errors are difficult to correct, resulting in the gradual accumulation of positioning errors, which in turn affects map accuracy. The present invention provides a high-precision mapping method for underground texture-poor spaces based on mobile-solid collaborative correction.

[0006] The technical solution provided by the embodiment of the present invention is as follows:

[0007] First aspect

[0008] An embodiment of the present invention provides a method for high-precision mapping of underground poor texture space based on mobile-solid collaborative correction, comprising:

[0009] S1: Acquire sensor data of a sensor node determined based on an underground lean texture spatial structure, wherein the sensor data includes structural data of the underground lean texture spatial structure and position data of the sensor node;

[0010] S2: Obtain the positioning data of the UAV in the underground texture-poor space, and calculate the initial position data of the UAV based on the positioning data;

[0011] S3: receiving sensor data through the drone, and generating drone position data by combining the drone initial position data and the drone lidar detection data;

[0012] S4: Correct the UAV position data through the segmented mobile-solid collaborative odometer;

[0013] S5: point cloud data used to describe the spatial structure of underground lean textures are obtained through the rectified UAV;

[0014] S6: fusing the sensor data, the initial position data of the UAV, the corrected position data of the UAV and the point cloud data to obtain fused data;

[0015] S7: Based on the fused data, each sensor node is used as a feature point in the mapping process to build a high-precision map of the underground poor texture space.

[0016] Second aspect

[0017] An embodiment of the present invention provides a high-precision mapping system for underground poor texture space based on mobile-solid collaborative correction, comprising:

[0018] processor;

[0019] A memory having computer-readable instructions stored thereon, which, when executed by a processor, implements a high-precision mapping method for underground poor texture space based on coordinated correction of movement and solid objects as in the first aspect.

[0020] The third aspect

[0021] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the method for high-precision mapping of underground poor texture space based on coordinated correction of movement and solid objects as described in the first aspect is implemented.

[0022] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0023] In the present invention, in a poor-texture underground environment, the positioning information of the UAV is used to calculate the initial position data of the UAV, ensuring a high initial accuracy when starting to build the map, reducing the subsequent correction pressure, transmitting sensor data to the UAV, and combining the UAV lidar detection data to generate relatively accurate position data, enhancing the stability of the data, and being able to obtain continuous and accurate map data. Through the segmented mobile-solid collaborative odometer correction, the accumulation of positioning errors is effectively reduced, and the accuracy of the odometer is improved. The calibrated UAV can obtain accurate point cloud data, describe the structural characteristics of the underground space in detail, and provide high-quality spatial data support for subsequent map construction. Through data fusion, the deviations of various data sources are eliminated, and high-precision fused data is generated to ensure the accuracy of map construction. By taking sensor nodes as feature points and integrating all fused data to build a high-precision underground map, accurate mapping of poor-texture underground space is achieved, effectively improving the efficiency of underground space management and application. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0025] Figure 1 A schematic diagram of a process flow of a method for high-precision mapping of underground poor texture space based on mobile-solid collaborative correction provided by an embodiment of the present invention;

[0026] Figure 2 A schematic diagram of a segmented mobile-fixed collaborative odometer correction process provided by an embodiment of the present invention;

[0027] Figure 3 A schematic diagram of data fusion provided by an embodiment of the present invention.

[0028] Figure 4 A schematic structural diagram of a high-precision mapping system for underground poor texture space based on mobile-solid collaborative correction provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0029] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0030] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0031] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0032] Reference Manual Attached Figure 1 , showing a flow chart of a method for high-precision mapping of underground poor texture space based on mobile-solid collaborative correction provided by an embodiment of the present invention.

[0033] The embodiment of the present invention provides a method for high-precision mapping of underground poor texture space based on mobile-solid collaborative correction, the method comprising:

[0034] S1: Acquire sensor data of sensor nodes determined based on the underground lean texture spatial structure, wherein the sensor data includes structural data of the underground lean texture spatial structure and position data of the sensor nodes.

[0035] Among them, sensor nodes refer to sensor devices (such as laser sensors, vibration sensors, etc.) arranged in underground spaces, which are used to collect structural data and positioning information of the environment. Sensor data is the information collected by sensor nodes, including structural characteristics of underground spaces (such as building vibrations, surface features) and location information of nodes, which are used to assist positioning and mapping.

[0036] In a possible implementation manner, S1 specifically includes:

[0037] Based on the underground texture-poor spatial structure, an adaptive optimal layout strategy is adopted to arrange sensor nodes and obtain sensor data.

[0038] Among them, the adaptive optimal layout strategy automatically adjusts the layout of sensor nodes according to the structural characteristics of the underground texture-poor space (such as tunnel shape, wall distance, etc.) to maximize the coverage and information acquisition effect.

[0039] It should be noted that through the adaptive optimal layout strategy, sensor nodes can efficiently cover key areas, avoid over-dense layout or missed areas, obtain as much environmental data as possible under limited resources, improve data utilization efficiency, and significantly improve the accuracy and completeness of underground space mapping.

[0040] In a possible implementation manner, the structural data of the underground texture-poor spatial structure includes building vibration data and building structure data.

[0041] Among them, building vibration data refers to the vibration information collected by sensor nodes, which reflects the vibration information generated by buildings or structures in underground space due to external factors (such as construction, geological activities, etc.). Building structure data is data obtained by sensors, which describes the shape, layout and other physical characteristics of underground buildings or space structures.

[0042] It should be noted that by collecting building vibration data and structural data, we can more accurately understand the physical state and stability of underground space and identify potential safety hazards. Real-time monitoring of building vibration data helps to correct errors in drone positioning, while structural data provides a stable reference benchmark for high-precision mapping, ensuring that the generated maps have higher reliability and accuracy.

[0043] In a possible implementation manner, the position data of the sensor node includes the position of the sensor node and the time when the sensor node sends the sensor data.

[0044] Specifically, by accurately recording the location information of sensor nodes and the time when data is sent, it is possible to perform spatiotemporal matching of data, filter and correct delays or errors in the data, and improve the accuracy and timeliness of underground maps.

[0045] It should be noted that by arranging sensor nodes in the underground texture-poor space, comprehensive structural data and location information can be obtained without relying on visual features, which effectively solves the problem of insufficient information in the texture-poor environment. The adaptive arrangement of sensor nodes ensures that key areas are covered, improves data stability and map accuracy, and provides a reliable data foundation for subsequent high-precision mapping.

[0046] S2: Obtain the positioning data of the UAV in the underground texture-poor space, and calculate the initial position data of the UAV based on the positioning data.

[0047] Among them, positioning data refers to the position information obtained by the UAV in the underground texture-poor space through sensors (such as lidar, inertial measurement unit, etc.), which is used to determine the position of the UAV in space. The initial position data is the specific position coordinates of the UAV at the starting point of mapping calculated based on the positioning data, which provides a reference point for subsequent path and map construction.

[0048] In a possible implementation manner, S2 is specifically:

[0049] The positioning data of the UAV in the underground texture-poor space is obtained, and the initial position data of the UAV is calculated using the accelerometer and gyroscope.

[0050] Among them, the accelerometer is a sensor that measures the acceleration changes of the drone in each axis, and infers the position changes by monitoring the acceleration during motion. The gyroscope is used to detect the angular velocity of the drone and help track its rotation and attitude changes.

[0051] It should be noted that by accurately obtaining the initial position data of the drone, a reliable reference point can be determined at the beginning of mapping, reducing the cumulative error of subsequent positioning, providing a stable starting point for high-precision mapping, and enabling the drone to navigate and map efficiently and accurately in underground environments lacking light and feature points, greatly improving the accuracy and consistency of the map.

[0052] S3: Receive sensor data through the drone and generate drone position data by combining the drone initial position data and the drone lidar detection data.

[0053] Among them, lidar detection data is the environmental depth and distance information obtained by the drone through lidar, which helps the drone identify spatial structures.

[0054] In a possible implementation, receiving sensor data through a drone specifically includes:

[0055] The communication channel between the drone and the sensor nodes is established through ZigBee low-power wireless communication.

[0056] Among them, ZigBee is a low-power, low-rate wireless communication protocol designed for short-distance, low-bandwidth device communication. It is often used in sensor networks and has the advantages of low power consumption, low cost, and flexible networking. It is very suitable for use in scenarios with limited power and low signal requirements. The communication channel refers to the wireless data transmission path established between the drone and the sensor node. Through this channel, the drone can receive environmental data sent by the sensor node in real time to support positioning and mapping.

[0057] Receive sensor data based on the communication channel.

[0058] It should be noted that by integrating sensor data, initial position and lidar detection data, high-precision drone positions can be continuously generated in underground environments lacking visual features. This not only enhances the stability and accuracy of positioning, but also reduces the errors caused by position drift, ensuring that drones can navigate and map underground spaces more accurately, and providing guarantees for the integrity and richness of details in the final map.

[0059] Reference Manual Attached Figure 2 , showing a schematic diagram of a segmented mobile-solid collaborative odometer correction process provided by an embodiment of the present invention.

[0060] like Figure 2, demonstrates a process of odometer correction by UAV in underground texture-poor space. The figure shows the deviation between the actual flight trajectory (dashed line) of the UAV in a complex path and the predetermined ideal path (solid line), as well as the fixed sensor node distribution and ZigBee communication path. The UAV receives the positioning information of the sensor nodes, realizes data transmission through ZigBee communication, and performs odometer correction at each predetermined position to reduce path deviation and finally achieve high-precision mapping.

[0061] S4: Correct the UAV position data through the segmented mobile-solid collaborative odometer.

[0062] Among them, the segmented mobile-fixed collaborative odometer correction is a positioning correction method based on odometer error control. This method combines mobile sensors (drones) and fixed sensor nodes to perform collaborative positioning correction in segments to reduce the accumulation of positioning errors.

[0063] In a possible implementation, S4 specifically includes:

[0064] S401: Get the relative position of the drone and the sensor node:

[0065]

[0066] Among them, P u Indicates the position of the drone in the world coordinate system. represents the coordinates of the sensor node in the world coordinate system, R represents the rotation matrix between the UAV and the sensor node, and T represents the translation vector between the UAV and the sensor node;

[0067] S402: According to the relative position of the UAV and the sensor node, the UAV position data is corrected through the segmented mobile-solid collaborative odometer.

[0068] In a possible implementation manner, S402 specifically includes:

[0069] When the timer interval of the segmented mobile-fixed cooperative odometer is greater than the preset timer interval and the distance between the sensor node and the drone is less than the preset distance, the drone position data is corrected.

[0070] It should be noted that the segmented mobile-solid collaborative correction effectively reduces the position drift of the UAV in narrow or complex underground spaces. Through segmented adjustment, the UAV can obtain accurate positioning data on each path. While avoiding cumulative errors, it improves the stability and accuracy of the overall positioning, ensuring that high-precision mapping can be achieved under various environmental conditions.

[0071] S5: Point cloud data used to describe the spatial structure of underground lean textures is obtained through the rectified UAV.

[0072] Among them, point cloud data is a three-dimensional spatial data set obtained by lidar or other ranging sensors, which usually contains a large number of spatial coordinate points (x, y, z). These points represent the surface and structure of objects in the environment and are densely and evenly distributed in space. Point cloud data is used to accurately depict the geometric shape of the surface and structure of objects, and is usually used in three-dimensional modeling, map construction and object recognition.

[0073] It should be noted that the corrected drone position data enhances the accuracy of the point cloud data, ensuring that the generated three-dimensional model is more accurate and can truly reflect the details and structure of the underground space. High-quality point cloud data supports detailed analysis and visualization of complex underground environments, improving the reliability and practicality of the map.

[0074] Reference Manual Attached Figure 3 , showing a data fusion schematic diagram provided by an embodiment of the present invention.

[0075] like Figure 3 , demonstrating the process of achieving high-precision mapping through data fusion. The figure shows that the UAV receives tunnel structure information through multiple fixed sensor nodes on the flight path. At the same time, the point cloud data generated by the lidar is also integrated into the positioning process of the UAV. The tunnel structure information of each sensor node and the lidar point cloud data are fused and processed on the UAV to form a high-precision underground space map, thereby providing accurate navigation reference for the UAV.

[0076] S6: Fusing the sensor data, the initial position data of the UAV, the corrected position data of the UAV and the point cloud data to obtain fused data.

[0077] Among them, data fusion refers to the comprehensive processing of multiple data from different sources (including sensor data, drone initial position data, corrected drone position data and point cloud data) to produce a more accurate and reliable result.

[0078] It should be noted that through data fusion, we can make full use of the information provided by various sensors to ensure the accuracy and consistency of map construction. The corrected drone position data is combined with the point cloud data to make the map have higher spatial resolution and accuracy.

[0079] S7: Based on the fused data, each sensor node is used as a feature point in the mapping process to build a high-precision map of the underground poor texture space.

[0080] It should be noted that using the various sensor nodes in the fused data as feature points for mapping can significantly improve the construction quality of high-precision maps. It not only makes up for the lack of visual features in the underground poor texture space, but also ensures the structural integrity and spatial resolution of the map, making the final high-precision map more realistic and accurate.

[0081] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0082] In the present invention, in a poor-texture underground environment, the positioning information of the UAV is used to calculate the initial position data of the UAV, ensuring a high initial accuracy when starting to build the map, reducing the subsequent correction pressure, transmitting sensor data to the UAV, and combining the UAV lidar detection data to generate relatively accurate position data, enhancing the stability of the data, and being able to obtain continuous and accurate map data. Through the segmented mobile-solid collaborative odometer correction, the accumulation of positioning errors is effectively reduced, and the accuracy of the odometer is improved. The calibrated UAV can obtain accurate point cloud data, describe the structural characteristics of the underground space in detail, and provide high-quality spatial data support for subsequent map construction. Through data fusion, the deviations of various data sources are eliminated, and high-precision fused data is generated to ensure the accuracy of map construction. By taking sensor nodes as feature points and integrating all fused data to build a high-precision underground map, accurate mapping of poor-texture underground space is achieved, effectively improving the efficiency of underground space management and application.

[0083] Reference Manual Attached Figure 4 , showing a structural schematic diagram of a high-precision mapping system for underground poor texture space based on mobile-solid collaborative correction provided by the present invention.

[0084] The present invention further provides a high-precision mapping system 20 for underground lean texture space based on mobile and fixed collaborative deviation correction, which is applied to the above-mentioned high-precision mapping method for underground lean texture space based on mobile and fixed collaborative deviation correction, comprising:

[0085] Processor 201.

[0086] The memory 202 stores computer-readable instructions. When the computer-readable instructions are executed by the processor 201, a high-precision mapping method for underground poor texture space based on mobile-solid collaborative correction as described in the method embodiment is implemented.

[0087] The high-precision underground poor texture space mapping system 20 based on moving-fixed collaborative correction provided by the present invention can execute the above-mentioned high-precision underground poor texture space mapping method based on moving-fixed collaborative correction, and achieve the same or similar technical effects. To avoid repetition, the present invention will not go into details.

[0088] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0089] In the present invention, in a poor-texture underground environment, the positioning information of the UAV is used to calculate the initial position data of the UAV, ensuring a high initial accuracy when starting to build the map, reducing the subsequent correction pressure, transmitting sensor data to the UAV, and combining the UAV lidar detection data to generate relatively accurate position data, enhancing the stability of the data, and being able to obtain continuous and accurate map data. Through the segmented mobile-solid collaborative odometer correction, the accumulation of positioning errors is effectively reduced, and the accuracy of the odometer is improved. The calibrated UAV can obtain accurate point cloud data, describe the structural characteristics of the underground space in detail, and provide high-quality spatial data support for subsequent map construction. Through data fusion, the deviations of various data sources are eliminated, and high-precision fused data is generated to ensure the accuracy of map construction. By taking sensor nodes as feature points and integrating all fused data to build a high-precision underground map, accurate mapping of poor-texture underground space is achieved, effectively improving the efficiency of underground space management and application.

[0090] It should be understood that the processor in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0091] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0092] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When a computer instruction or computer program is loaded or executed on a computer, a process or function according to an embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0093] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0094] In the present invention, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0095] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0096] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0097] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0098] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0099] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0100] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0101] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0102] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, a high-precision mapping method for underground poor texture space based on coordinated correction of movement and solid objects as described in a method embodiment is implemented.

[0103] A computer-readable storage medium provided by the present invention can realize the steps and effects of the method for high-precision mapping of underground poor texture space based on coordinated correction of movement and solidness in the above-mentioned method embodiment. To avoid repetition, the present invention will not go into details.

[0104] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0105] In the present invention, in a poor-texture underground environment, the positioning information of the UAV is used to calculate the initial position data of the UAV, ensuring a high initial accuracy when starting to build the map, reducing the subsequent correction pressure, transmitting sensor data to the UAV, and combining the UAV lidar detection data to generate relatively accurate position data, enhancing the stability of the data, and being able to obtain continuous and accurate map data. Through the segmented mobile-solid collaborative odometer correction, the accumulation of positioning errors is effectively reduced, and the accuracy of the odometer is improved. The calibrated UAV can obtain accurate point cloud data, describe the structural characteristics of the underground space in detail, and provide high-quality spatial data support for subsequent map construction. Through data fusion, the deviations of various data sources are eliminated, and high-precision fused data is generated to ensure the accuracy of map construction. By taking sensor nodes as feature points and integrating all fused data to build a high-precision underground map, accurate mapping of poor-texture underground space is achieved, effectively improving the efficiency of underground space management and application.

[0106] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0107] There are a few points to note:

[0108] (1) The drawings of the embodiments of the present invention only involve structures related to the embodiments of the present invention. Other structures may refer to conventional designs.

[0109] (2) For the sake of clarity, in the drawings used to describe the embodiments of the present invention, the thickness of layers or regions is exaggerated or reduced, that is, these drawings are not drawn according to the actual scale. It is understood that when an element such as a layer, film, region or substrate is referred to as being "on" or "under" another element, the element may be "directly" "on" or "under" the other element or there may be intermediate elements.

[0110] (3) In the absence of conflict, the embodiments of the present invention and the features therein may be combined with each other to obtain new embodiments.

[0111] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A high-precision mapping method for underground poor texture space based on coordinated correction of moving and solid objects, characterized in that: include: S1: Acquire sensor data of a sensor node determined based on an underground lean texture spatial structure, wherein the sensor data includes structural data of the underground lean texture spatial structure and position data of the sensor node; S2: Acquire positioning data of the UAV in the underground texture-poor space, and calculate initial position data of the UAV based on the positioning data; S3: receiving the sensor data through the drone, and generating drone position data by combining the drone initial position data and the drone laser radar detection data; S4: Correcting the position data of the UAV by using a segmented mobile-solid cooperative odometer; S5: acquiring point cloud data for describing the underground lean texture spatial structure through the rectified UAV; S6: fusing the sensor data, the initial position data of the drone, the corrected position data of the drone and the point cloud data to obtain fused data; S7: Based on the fused data, each sensor node is used as a feature point in the mapping process to establish a high-precision map of the underground poor texture space.

2. The method for high-precision mapping of underground poor texture space based on coordinated deviation correction of moving and solid objects according to claim 1 is characterized in that: The S1 is specifically: Based on the underground poor texture spatial structure, the sensor nodes are arranged using an adaptive optimal arrangement strategy, and the sensor data is acquired.

3. The method for high-precision mapping of underground poor texture space based on coordinated deviation correction of mobile and solid objects according to claim 1 is characterized in that: The structural data of the underground lean texture spatial structure includes building vibration data and building structure data.

4. The method for high-precision mapping of underground poor texture space based on coordinated deviation correction of mobile and solid objects according to claim 1 is characterized in that: The location data of the sensor node includes the location of the sensor node and the time when the sensor node sends the sensor data.

5. The method for high-precision mapping of underground poor texture space based on coordinated deviation correction of mobile and solid objects according to claim 1 is characterized in that: The S2 is specifically: The positioning data of the UAV in the underground texture-poor space is obtained, and the initial position data of the UAV is calculated using an accelerometer and a gyroscope.

6. The method for high-precision mapping of underground poor texture space based on coordinated deviation correction of moving and solid objects according to claim 1 is characterized in that: The receiving the sensor data by the drone specifically includes: Establishing a communication channel between the drone and the sensor node through ZigBee low-power wireless communication; The sensor data is received based on the communication channel.

7. The method for high-precision mapping of underground poor texture space based on coordinated deviation correction of moving and solid objects according to claim 1 is characterized in that: The S4 specifically includes: S401: Obtaining the relative position of the UAV and the sensor node: ； Among them, P u Indicates the position of the drone in the world coordinate system. represents the coordinates of the sensor node in the world coordinate system, R represents the rotation matrix between the UAV and the sensor node, and T represents the translation vector between the UAV and the sensor node; S402: According to the relative position of the UAV and the sensor node, the position data of the UAV is corrected by using a segmented mobile-fixed cooperative odometer.

8. The method for high-precision mapping of underground poor texture space based on coordinated deviation correction of moving and solid objects according to claim 1 is characterized in that: The S402 is specifically as follows: When the timer interval of the segmented mobile-fixed cooperative odometer is longer than the preset timer interval and the distance between the sensor node and the drone is less than the preset distance, the drone position data is corrected.

9. A high-precision mapping system for underground poor texture space based on mobile and solid collaborative correction, characterized in that: include: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, a high-precision mapping method for underground poor texture space based on coordinated correction of movement and solid objects as described in any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, a high-precision mapping method for underground poor texture space based on mobile-solid collaborative correction as described in any one of claims 1 to 8 is implemented.

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