An underground low-texture space high-precision mapping method based on solid displacement coordination and deviation correction

Abstract

The application provides a kind of underground poor texture space high-precision mapping method based on solid displacement coordination rectification, relating to map construction technical field, method includes: obtaining the sensor data of sensor node based on the determination of underground poor texture space structure;Calculate the initial position data of unmanned aerial vehicle;Through unmanned aerial vehicle receives sensor data, and generate unmanned aerial vehicle position data in combination with initial position data of unmanned aerial vehicle and unmanned aerial vehicle laser radar detection data;Through segmented solid displacement coordination odometry, correct unmanned aerial vehicle position data;Obtain point cloud data for describing underground poor texture space structure;Sensor data, initial position data of unmanned aerial vehicle, corrected unmanned aerial vehicle position data and point cloud data are fused to obtain fusion data, and each sensor node is used as feature point in mapping process, and the high-precision map of underground poor texture space is established.The application can accurately establish the high-precision map of underground poor texture space, and effectively improve the efficiency of underground space management and application.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of map construction, in particular to a high-precision mapping method for underground poor-texture space based on solid displacement coordination rectification. BACKGROUND

[0002] The high-precision mapping method for underground poor-texture space is a precise positioning and mapping method for underground scenes. Underground poor-texture space is an area in the underground environment that lacks rich visual features (i.e., texture information), such as tunnels, mines, or subway stations. Due to insufficient lighting, monotonous colors, and similar surface structures, such spaces often lack feature points for visual recognition, making image-based positioning and mapping difficult. The high-precision mapping method for underground poor-texture space can achieve high-precision mapping in low-texture underground spaces.

[0003] The high-precision mapping method for underground poor-texture space based on solid displacement coordination rectification has important significance in modern urban planning, infrastructure construction, environmental protection, mining, disaster rescue, archaeological research, and intelligent city development. In particular, in the development process of intelligent cities, high-precision underground maps are crucial for intelligent transportation and intelligent facility management. The construction and maintenance of some urban infrastructure require detailed underground pipe networks and facility maps to reduce construction errors and accident risks.

[0004] However, in underground poor-texture space, due to insufficient lighting and single texture, visual sensors have difficulty extracting enough feature points, resulting in low positioning and mapping accuracy. Traditional simultaneous localization and mapping techniques commonly used in underground environments have poor data stability in narrow or complex terrain conditions, making it difficult to obtain continuous and accurate map data. Due to the narrow and long nature of underground tunnels, loop detection is difficult to implement, and odometer errors are difficult to correct, leading to gradual accumulation of positioning errors and affecting map accuracy. SUMMARY

[0005] To solve the technical problems of low positioning and mapping accuracy in underground poor-texture space due to insufficient lighting and single texture, and the difficulty of loop detection and odometer error correction in traditional simultaneous localization and mapping techniques commonly used in underground environments, the present application provides a high-precision mapping method for underground poor-texture space based on solid displacement coordination rectification.

[0006] The technical solutions provided by the embodiments of the present application are as follows:

[0007] First aspect

[0008] The embodiment of the present application provides a high-precision mapping method for an underground poor-texture space based on solid displacement coordination and deviation correction, comprising the following steps:

[0009] S1: obtaining sensor data of a sensor node determined based on an underground poor-texture space structure, wherein the sensor data comprises structure data of the underground poor-texture space structure and position data of the sensor node;

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

[0011] S3: receiving the sensor data by the UAV, and generating UAV position data by combining the initial position data of the UAV and laser radar detection data of the UAV;

[0012] S4: correcting the UAV position data by using a segmented solid displacement coordination odometer;

[0013] S5: obtaining point cloud data for describing the structure of the underground poor-texture space by using the corrected UAV;

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

[0015] S7: regarding each sensor node as a feature point in a mapping process according to the fused data, and establishing a high-precision map of the underground poor-texture space.

[0016] The second aspect

[0017] The embodiment of the present application provides a high-precision mapping system for an underground poor-texture space based on solid displacement coordination and deviation correction, comprising the following steps:

[0018] A processor;

[0019] A memory, wherein the memory stores computer readable instructions, and the computer readable instructions are executed by the processor to realize the high-precision mapping method for the underground poor-texture space based on the solid displacement coordination and the deviation correction according to the first aspect.

[0020] The third aspect

[0021] The embodiment of the present application provides a computer readable storage medium, which stores a computer program, and the program is executed by the processor to realize the high-precision mapping method for the underground poor-texture space based on the solid displacement coordination and the deviation correction according to the first aspect.

[0022] The technical scheme provided by the embodiment of the present application has at least the following beneficial effects:

[0023] In the present application, in the poor texture underground environment, the initial position data of the unmanned aerial vehicle is calculated by using the positioning information of the unmanned aerial vehicle, so as to ensure high initial accuracy when starting to build a map, reduce the subsequent correction pressure, transmit the sensor data to the unmanned aerial vehicle, combine the laser radar detection data of the unmanned aerial vehicle, generate relatively accurate position data, enhance the stability of the data, obtain continuous and accurate map data, effectively reduce the accumulation of positioning error through segmented solid displacement cooperative odometer correction, improve the accuracy of the odometer, and the corrected unmanned aerial vehicle can obtain accurate point cloud data, describe the structural characteristics of the underground space in detail, provide high-quality spatial data support for subsequent map construction, eliminate the deviation of each data source through data fusion, generate high-precision fusion data, ensure the accuracy of the mapping, take the sensor node as a feature point, integrate all the fusion data to construct an underground high-precision map, realize accurate mapping of the poor texture underground space, and effectively improve the efficiency of underground space management and application. BRIEF DESCRIPTION OF DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0025] Figure 1 A flowchart of a high-precision mapping method for poor texture underground space based on solid displacement cooperative correction provided by the embodiment of the present application is shown.

[0026] Figure 2 A segmented solid displacement cooperative odometer correction process diagram provided by the embodiment of the present application is shown.

[0027] Figure 3 A data fusion diagram provided by the embodiment of the present application is shown.

[0028] Figure 4 A structure diagram of a high-precision mapping system for poor texture underground space based on solid displacement cooperative correction provided by the embodiment of the present application is shown. DETAILED DESCRIPTION

[0029] The technical solutions in the present application will be described below with reference to the drawings.

[0030] In the embodiments of the present application, the words such as "example", "for example" are used to represent an example, illustration, or description. Any embodiment or design scheme described as "example" in the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the word "example" is intended to present the concept in a specific manner. In addition, in the embodiments of the present application, the meaning expressed by "and / or" can be both, or can be one of the two.

[0031] To make the technical problems, technical solutions and advantages to be solved by the present application clearer, the following will be described in detail in conjunction with the drawings and specific embodiments.

[0032] Referring to the drawings accompanying the Figure 1 , a flowchart of a high-precision mapping method for underground poor-texture space based on solid displacement coordination and correction is shown.

[0033] The embodiments of the present application provide a high-precision mapping method for underground poor-texture space based on solid displacement coordination and correction, which comprises the following steps:

[0034] S1: Obtain sensor data of a sensor node based on an underground poor-texture space structure, wherein the sensor data comprises structure data of the underground poor-texture space structure and position data of the sensor node.

[0035] Wherein, the sensor node refers to a sensing device (such as a laser sensor, a vibration sensor, etc.) arranged in the underground space, which is used to collect structure data and positioning information of the environment. The sensor data is the information collected by the sensor node, which contains the structural features (such as building vibration, surface features) of the underground space and the position information of the node, which is used to assist positioning and mapping.

[0036] In a possible implementation, S1 is specifically:

[0037] Based on the underground poor-texture space structure, the sensor node is arranged by using an adaptive optimal arrangement strategy, and the sensor data is obtained.

[0038] Wherein, the adaptive optimal arrangement strategy automatically adjusts the arrangement position of the sensor node according to the structural features (such as tunnel shape, wall distance, etc.) of the underground poor-texture space, so as to maximize the coverage range and information acquisition effect.

[0039] It should be noted that through the adaptive optimal arrangement strategy, the sensor node can efficiently cover the key area, avoid the situation of over-dense arrangement or omission area, obtain as much environmental data as possible under the condition of limited resources, improve the utilization efficiency of data, and significantly improve the accuracy and integrity of the underground space mapping.

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

[0041] The building vibration data refers to the vibration information of the building or structure in the underground space caused by external factors (such as construction, geological activity, etc.) collected by the sensor node, and the building structure data refers to the data obtained by the sensor to describe the shape, layout, and other physical characteristics of the underground building or space structure.

[0042] It should be noted that by collecting building vibration data and structure data, the physical state and stability of the underground space can be more accurately understood, and potential safety hazards can be identified. Real-time monitoring of building vibration data helps to correct errors in unmanned aerial vehicle positioning, and structure data provides a stable reference benchmark for high-precision mapping, ensuring that the generated map has higher reliability and accuracy.

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

[0044] Specifically, by accurately recording the position information and data sending time of the sensor node, the data can be spatio-temporally matched, filtered and corrected for delays or errors, and the accuracy and timeliness of the underground map can be improved.

[0045] It should be noted that by arranging sensor nodes in the underground poor-textured space, comprehensive structure data and position information can be obtained, which does not rely on visual features, effectively solving the problem of insufficient information in the poor-textured environment. The adaptive arrangement of the sensor nodes ensures that the key areas are covered, improving the stability of the data and the accuracy of the map, and providing a reliable data foundation for subsequent high-precision mapping.

[0046] S2: Obtain positioning data of the unmanned aerial vehicle in the underground poor-textured space, and calculate initial position data of the unmanned aerial vehicle based on the positioning data.

[0047] The positioning data refers to the position information obtained by the unmanned aerial vehicle in the underground poor-textured space through sensors (such as laser radar, inertial measurement unit, etc.), which is used to determine the position of the unmanned aerial vehicle in the space. The initial position data is the specific position coordinates of the unmanned aerial vehicle 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, S2 is specifically:

[0049] Obtain the positioning data of the unmanned aerial vehicle in the underground poor-textured space, and calculate the initial position data of the unmanned aerial vehicle using an acceleration machine and a gyroscope.

[0050] Wherein, the accelerometer is a sensor for measuring the acceleration change of the UAV in each axial direction, and the gyroscope is used for detecting the angular velocity of the UAV to help track the change of rotation and attitude of the UAV.

[0051] It should be noted that by accurately obtaining the initial position data of the UAV, 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, enabling the UAV to efficiently and accurately navigate and map in the underground environment lacking of light and feature points, and greatly improving the accuracy and consistency of the map.

[0052] S3: receiving sensor data by the UAV, and generating UAV position data in combination with the initial position data of the UAV and the laser radar detection data of the UAV.

[0053] Wherein, the laser radar detection data is the environmental depth and distance information obtained by the UAV through the laser radar to help the UAV identify the spatial structure.

[0054] In a possible implementation, the sensor data is received by the UAV, specifically including:

[0055] The communication channel between the UAV and the sensor node is established by ZigBee low-power wireless communication mode.

[0056] Wherein, ZigBee is a low-power and low-rate wireless communication protocol designed for short-distance and low-bandwidth device communication, commonly used in sensor networks, with the advantages of low power consumption, low cost, flexible networking, etc., and is very suitable for use in power-limited and low-signal-demand scenarios, and the communication channel refers to the wireless data transmission path established between the UAV and the sensor node, through which the UAV can receive the environmental data sent by the sensor node in real time to support positioning and mapping.

[0057] The sensor data is received based on the communication channel.

[0058] It should be noted that by integrating the sensor data, the initial position and the laser radar detection data, the high-precision UAV position is continuously generated in the underground environment lacking of visual features, which not only enhances the stability and accuracy of positioning, but also reduces the error caused by position drift, ensuring that the navigation and mapping of the UAV in the underground space are more accurate, and providing protection for the integrity and detail richness of the final map.

[0059] Reference is made to the accompanying drawings Figure 2 , which shows a segmented solid-moving cooperative odometry correction process schematic diagram provided by the embodiment of the application.

[0060] As Figure 2, shows a process of odometer correction by UAV in underground sparse texture space, the figure shows the deviation between the actual flight trajectory (dotted line) of the UAV in the 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 node, realizes data transmission through ZigBee communication, and corrects the odometer at each predetermined position, reduces the path deviation, and finally realizes high-precision mapping.

[0061] S4: correcting the UAV position data by segmented solid displacement and odometer correction.

[0062] Among them, the segmented solid displacement and odometer correction is a positioning correction method based on odometer error control, which combines mobile sensors (UAV) and fixed sensor nodes to correct the positioning cooperatively in segments to reduce the accumulation of positioning errors.

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

[0064] S401: obtaining the relative position of the UAV and the sensor node:

[0065]

[0066] Among them, P u represents the position of the UAV 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: correcting the UAV position data by segmented solid displacement and odometer correction according to the relative position of the UAV and the sensor node.

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

[0069] When the timer interval length of the segmented solid displacement and odometer correction is greater than the preset timer interval length and the distance between the sensor node and the UAV is less than the preset distance, the UAV position data is corrected.

[0070] It should be noted that the segmented solid displacement and odometer correction effectively reduces the position drift of the UAV in the narrow or complex underground space. Through segmented adjustment, the UAV can obtain accurate positioning data on each path, avoid cumulative errors, improve the stability and accuracy of the overall positioning, and ensure that high-precision mapping can be realized under various environmental conditions.

[0071] S5: obtaining point cloud data for describing the structure of the underground sparse texture space by the corrected UAV.

[0072] wherein the point cloud data is a three-dimensional spatial dataset obtained through a laser radar or other ranging sensors, usually containing a large number of spatial coordinate points (x, y, z) representing the surfaces and structures of objects in the environment, distributed densely and uniformly in space, and the point cloud data is used to accurately depict the geometry of object surfaces and structures, commonly applied to three-dimensional modeling, map construction and object recognition.

[0073] It should be noted that the corrected unmanned aerial vehicle 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 structures of 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 Description Figure 3 , a data fusion schematic diagram provided by an embodiment of the application is shown.

[0075] As Figure 3 , the process of achieving high-precision mapping through data fusion is demonstrated, and the figure shows that the unmanned aerial vehicle receives tunnel structure information through multiple fixed sensor nodes on the flight path, at the same time, the point cloud data generated by the laser radar is also integrated into the positioning process of the unmanned aerial vehicle, the tunnel structure information of each sensor node and the laser radar point cloud data are fused on the unmanned aerial vehicle, forming a high-precision underground space map, thereby providing accurate navigation reference for the unmanned aerial vehicle.

[0076] S6: Fuse the sensor data, initial unmanned aerial vehicle position data, corrected unmanned aerial vehicle position data and point cloud data to obtain fused data.

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

[0078] It should be noted that through data fusion, the information provided by various sensors can be fully utilized to ensure the accuracy and consistency of map construction, and the corrected unmanned aerial vehicle position data combined with the point cloud data makes the map have higher spatial resolution and accuracy.

[0079] S7: According to the fused data, each sensor node is taken as a feature point in the mapping process to establish a high-precision map of the underground poor-textured space.

[0080] It should be noted that using each sensor node in the fusion data as a feature point for mapping can significantly improve the construction quality of the high-precision map, not only compensating for the lack of visual features in the underground poor-texture space, but also ensuring the structural integrity and spatial resolution of the map, making the finally generated high-precision map more realistic and accurate.

[0081] The technical scheme provided by the embodiment of the application has at least the following beneficial effects:

[0082] In the present application, in the poor-texture underground environment, the initial position data of the unmanned aerial vehicle is calculated using the positioning information of the unmanned aerial vehicle, ensuring that the initial accuracy is high when starting to map, reducing the subsequent correction pressure, transmitting the sensor data to the unmanned aerial vehicle, and combining the laser radar detection data of the unmanned aerial vehicle to generate relatively accurate position data, enhancing the stability of the data, and obtaining continuous and accurate map data, effectively reducing the accumulation of positioning errors through segmented solid displacement cooperative correction, improving the accuracy of the odometer, and the corrected unmanned aerial vehicle can obtain accurate point cloud data, detailing the structural features of the underground space, providing high-quality spatial data support for subsequent map construction, eliminating the deviation of each data source through data fusion, generating high-precision fusion data, ensuring the accuracy of mapping, and constructing an underground high-precision map by taking the sensor nodes as feature points and integrating all fusion data, realizing accurate mapping of the poor-texture underground space, and effectively improving the efficiency of underground space management and application.

[0083] Reference is made to the accompanying drawings Figure 4 The accompanying drawings show a structure schematic diagram of the underground poor-texture space high-precision mapping system based on solid displacement cooperative correction provided by the present application.

[0084] The present application also provides an underground poor-texture space high-precision mapping system 20 based on solid displacement cooperative correction, which is applied to the above-mentioned underground poor-texture space high-precision mapping method based on solid displacement cooperative correction, and comprises:

[0085] The processor 201.

[0086] The memory 202, the memory 202 stores computer readable instructions, and the computer readable instructions are executed by the processor 201 to realize the underground poor-texture space high-precision mapping method based on solid displacement cooperative correction as in the method embodiment.

[0087] The underground poor-texture space high-precision mapping system 20 based on solid displacement cooperative correction provided by the present application can execute the above-mentioned underground poor-texture space high-precision mapping method based on solid displacement cooperative correction, and achieve the same or similar technical effects, in order to avoid repetition, the present application will not be repeated.

[0088] The technical scheme provided by the embodiment of the application has at least the following beneficial effects:

[0089] In the present application, in the poor texture underground environment, the initial position data of the unmanned aerial vehicle is calculated by using the positioning information of the unmanned aerial vehicle, so as to ensure high initial accuracy when starting mapping, reduce the subsequent correction pressure, transmit the sensor data to the unmanned aerial vehicle, combine the laser radar detection data of the unmanned aerial vehicle, generate relatively accurate position data, enhance the stability of the data, obtain continuous and accurate map data, effectively reduce the accumulation of positioning error through the segmented fixed coordinate and odometer correction, improve the accuracy of the odometer, and the corrected unmanned aerial vehicle can obtain accurate point cloud data, and the structure characteristics of the underground space are described in detail, so as to provide high-quality spatial data support for subsequent map construction, eliminate the deviation of each data source through data fusion, generate high-precision fusion data, ensure the accuracy of mapping, take the sensor node as a feature point, integrate all fusion data to construct an underground high-precision map, realize accurate mapping of the poor texture underground space, and effectively improve the efficiency of underground space management and application.

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

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

[0092] The above-described embodiments can be implemented in whole or in part by software, hardware (such as a circuit), firmware, or any combination thereof. When implemented in software, the above-described embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions according to the embodiments of the present application are wholly or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center through a wired (for example, infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server, data center, etc. containing one or more available medium collections. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state disk.

[0093] It should be understood that the term "and / or" herein merely describes an association relationship of associated objects, which means that there can be three relationships, for example, A and / or B can represent three cases of A alone, A and B together, and B alone, where A and B can be singular or plural. In addition, the character " / " herein generally represents an "or" relationship between the associated objects before and after it, but it can also represent an "and / or" relationship, which can be understood in the context before and after it.

[0094] In the present application, "at least one" means one or more, and "multiple" means two or more. "At least one of the following" or the like means any combination of the items, including any combination of single item or multiple items. For example, at least one of a, b, or c can represent a, b, c, a-b, a-c, b-c, or a-b-c, where a, b, and c can be single or multiple.

[0095] It should be understood that in various embodiments of the present application, the size of the sequence number of the above-described processes does not mean the order of execution, and the execution order of the processes should be determined by their functions and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

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

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

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

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

[0100] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically independently, or two or more units can be integrated into one unit.

[0101] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or parts of the present application that essentially contribute to the prior art or parts of the technical solutions can be embodied in the form of a software product, which is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various storage media that can store program codes.

[0102] The embodiment of the present application provides a computer readable storage medium, which stores a computer program, and the program is executed by a processor to realize the underground high-precision mapping method based on solid displacement and deviation correction in a poor-textured space according to the method embodiment.

[0103] The computer readable storage medium provided by the present application can realize the steps and effects of the underground high-precision mapping method based on solid displacement and deviation correction in a poor-textured space according to the method embodiment, and the present application will not be repeated here to avoid repetition.

[0104] The technical solutions provided by the embodiment of the present application have at least the following beneficial effects:

[0105] In the present application, in a poor-textured underground environment, the initial position data of the unmanned aerial vehicle is calculated by using the positioning information of the unmanned aerial vehicle, so that high initial accuracy is ensured when mapping starts, the subsequent correction pressure is reduced, the sensor data is transmitted to the unmanned aerial vehicle, and the relatively accurate position data is generated by combining the laser radar detection data of the unmanned aerial vehicle, the stability of the data is enhanced, the continuous and accurate map data can be obtained, the positioning error accumulation is effectively reduced by the segmented solid displacement and odometer correction, the accuracy of the odometer is improved, the unmanned aerial vehicle after correction can obtain accurate point cloud data, and the structure characteristics of the underground space are described in detail, so as to provide high-quality spatial data support for subsequent map construction, the deviation of each data source is eliminated by data fusion, high-precision fusion data is generated, the accuracy of mapping is ensured, the sensor nodes are taken as feature points, all fusion data is integrated to construct an underground high-precision map, and accurate mapping of the poor-textured underground space is realized, so that the efficiency of underground space management and application is effectively improved.

[0106] The above merely illustrates the specific embodiments of the present application, and the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope disclosed by the present application, which should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0107] The following points need to be explained:

[0108] (1) The drawings of the embodiments of the present application only involve the structures involved in the embodiments of the present application, and other structures can refer to the usual design.

[0109] (2) In the drawings for describing the embodiments of the present application, the thickness of a layer or region is exaggerated or reduced for clarity, that is, the drawings are not drawn according to the actual proportion. It can be understood that when an element such as a layer, a film, a region or a substrate is referred to as being "on" or "under" another element, the element can be "directly" on or under the other element or there can be an intermediate element.

[0110] (3) In the case of no conflict, the embodiments of the present application and the features in the embodiments can be combined with each other to obtain new embodiments.

[0111] The above merely illustrates the specific embodiments of the present application, and the protection scope of the present application is not limited thereto, and the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A high-precision mapping method for underground poor texture space based on mobile and solid collaborative correction, characterized by: include: S1: Acquire sensor data of a sensor node determined based on an underground texture-poor spatial structure, wherein the sensor data includes structural data of the underground texture-poor 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 lidar detection data; S4: Correcting the UAV position data using a segmented mobile-solid collaborative 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 UAV, the corrected UAV position data, 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 a mapping process to establish a high-precision map of the underground texture-poor space; Wherein, the S4 specifically includes: S401: Obtain 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: Correcting the drone position data using a segmented mobile-solid collaborative odometer based on the relative position of the drone and the sensor node; The S402 is specifically as follows: When the timer interval of the segmented mobile-fixed cooperative odometer is longer than a preset timer interval and the distance between the sensor node and the drone is less than a preset distance, the drone position data is corrected.

2. 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 S1 is specifically: Based on the underground texture-poor 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 mobile 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. A high-precision mapping system for underground poor texture space based on mobile and solid collaborative correction, characterized by: include: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method for high-precision mapping of underground poor texture space based on mobile-solid collaborative correction as described in any one of claims 1 to 6 is implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for high-precision mapping of underground poor texture space based on mobile-solid collaborative correction as described in any one of claims 1 to 6 is implemented.

Images