CN113487583A - Underground roadway surface deformation detection system based on 3D point cloud slice - Google Patents
Underground roadway surface deformation detection system based on 3D point cloud slice Download PDFInfo
- Publication number
- CN113487583A CN113487583A CN202110812819.8A CN202110812819A CN113487583A CN 113487583 A CN113487583 A CN 113487583A CN 202110812819 A CN202110812819 A CN 202110812819A CN 113487583 A CN113487583 A CN 113487583A
- Authority
- CN
- China
- Prior art keywords
- point cloud
- explosion
- proof
- deformation
- camera
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 23
- 230000000007 visual effect Effects 0.000 claims abstract description 7
- 238000013507 mapping Methods 0.000 claims abstract description 6
- 238000000034 method Methods 0.000 claims description 12
- 238000004880 explosion Methods 0.000 claims description 8
- 239000011159 matrix material Substances 0.000 claims description 7
- 238000004891 communication Methods 0.000 claims description 6
- 238000001914 filtration Methods 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 6
- 238000005457 optimization Methods 0.000 claims description 5
- 239000005341 toughened glass Substances 0.000 claims description 4
- 239000000284 extract Substances 0.000 claims description 3
- 238000005516 engineering process Methods 0.000 abstract description 4
- 238000005259 measurement Methods 0.000 abstract description 4
- 239000003245 coal Substances 0.000 description 4
- 238000004519 manufacturing process Methods 0.000 description 3
- 238000012544 monitoring process Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 238000009423 ventilation Methods 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000000691 measurement method Methods 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000011435 rock Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/50—Depth or shape recovery
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/90—Determination of colour characteristics
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10016—Video; Image sequence
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10028—Range image; Depth image; 3D point clouds
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10048—Infrared image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
- G06T2207/30132—Masonry; Concrete
Landscapes
- Engineering & Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Quality & Reliability (AREA)
- Length Measuring Devices By Optical Means (AREA)
Abstract
本发明涉及巷道表面的检测领域，公开了一种基于3D点云切片的井下巷道表面变形检测系统，由防爆移动机器人、防爆双目深度相机、高性能计算机及软件系统组成。防爆双目深度相机实时采集井下巷道表面图像，利用WiFi模块无线传输至计算机中进行分析处理。软件系统基于视觉同时定位与建图技术生成稠密3D点云图；利用3D点云切片技术，得到巷道表面每层点云每个位点的空间坐标，可获取变形位置、变形大小、变形几何形状。本发明无需人工检测井下巷道表面，仅搭载防爆双目深度相机的防爆移动机器人自主移动整个巷道一至两次，即可完成井下巷道表面3D点云稠密建图、点云切片和变形检测。本系统自动化程度高、检测速度快、测量精度高。
The invention relates to the field of roadway surface detection, and discloses an underground roadway surface deformation detection system based on 3D point cloud slices, which is composed of an explosion-proof mobile robot, an explosion-proof binocular depth camera, a high-performance computer and a software system. The explosion-proof binocular depth camera collects the surface image of the underground roadway in real time, and uses the WiFi module to wirelessly transmit it to the computer for analysis and processing. The software system generates dense 3D point cloud images based on visual simultaneous positioning and mapping technology; using 3D point cloud slicing technology, the spatial coordinates of each point of each layer of point cloud on the road surface can be obtained, and the deformation position, deformation size, and deformation geometry can be obtained. The invention does not need to manually detect the surface of the underground roadway, and only the explosion-proof mobile robot equipped with the explosion-proof binocular depth camera moves the entire roadway autonomously once or twice to complete the 3D point cloud dense mapping, point cloud slicing and deformation detection of the underground roadway surface. The system has a high degree of automation, fast detection speed and high measurement accuracy.
Description
Technical Field
The invention relates to the field of detection of roadway surfaces, in particular to an underground roadway surface deformation detection system based on 3D point cloud slices.
Background
The underground roadway plays a role in lifting in coal mine safety production, supports the whole coal bed and provides necessary conditions for mining lifting, transportation, ventilation, drainage and power supply, and the deformation of the surface of the underground roadway refers to the change of the shape and the size of a roadway rock stratum under the action of external force factors. The deformation and damage of the surface of the underground tunnel can block underground traffic, increase the ventilation resistance of a mine, damage production equipment, cause casualties and seriously threaten the safety production of a coal mine, so the tunnel surface monitoring is one of the important monitoring targets of the coal mine.
At present, instruments for measuring the surface deformation of the underground roadway at home and abroad can be divided into a mechanical measuring instrument and an electrical measuring instrument, and the instruments mainly adopt an acoustic ranging method, a laser ranging method, an optical surveying instrument, a close-range photogrammetry method and the like. These methods for monitoring roadway deformation often need to manually carry equipment to move back and forth in an underground roadway for detection, which not only consumes long time and has low automation degree, but also has unstable measurement precision. With the development of underground unmanned intelligence, a new method is urgently needed to overcome the defects of the traditional roadway surface deformation measurement method.
Disclosure of Invention
In order to solve the above mentioned shortcomings in the background art, the present invention provides a system for detecting surface deformation of an underground roadway based on a 3D point cloud slice.
The purpose of the invention can be realized by the following technical scheme:
the utility model provides a underworkings surface deformation detecting system based on 3D point cloud section, includes explosion-proof mobile robot, its characterized in that, install explosion-proof binocular depth camera on the explosion-proof mobile robot, explosion-proof binocular depth camera is connected with the high performance computer through the wiFi module, includes the software system among the high performance computer.
Furthermore, the explosion-proof binocular depth camera is installed on a vertical rod support at the front end of the body of the explosion-proof mobile robot, and the angle is adjustable.
Furthermore, the anti-explosion mobile robot is composed of an anti-explosion vehicle body, a power supply, a wheel driving control system and a communication system, wherein the power supply is connected with the wheel driving control system and the communication system to realize power supply, and the anti-explosion mobile robot can walk autonomously or through remote control when in use.
Further, explosion-proof two mesh degree of depth cameras comprise explosion-proof toughened glass, RGB camera, infrared dot matrix transmitter and two mesh infrared cameras, and two mesh infrared cameras comprise left infrared camera and right infrared camera, and RGB camera and infrared dot matrix transmitter set up between left infrared camera and right infrared camera.
Further, explosion-proof mobile robot places in the underworkings, and the RGB image in the underworkings is gathered to the RGB camera, and binocular infrared camera gathers the degree of depth information that corresponds with RGB in corresponding the scene.
Furthermore, the explosion-proof binocular depth camera performs one to two times of image acquisition on the whole underground tunnel, video image information is transmitted to a high-performance computer in real time through a WiFi module, and a software system in the high-performance computer automatically processes the acquired images based on a VSLAM algorithm to generate dense 3D point cloud images.
Further, the software system is based on a VSLAM algorithm, and the algorithm process comprises sensor information reading, front-end visual odometry, rear-end optimization, loop detection, image building, point cloud slicing and comparison.
Further, the software system carries out through filtering on the generated dense 3D point cloud picture according to three dimensionality directions of x, y and z to filter noise points, carries out voxel filtering on the result to reduce the number of point clouds, extracts a clear 3D point cloud picture of the underground tunnel, automatically carries out point cloud slicing according to one dimensionality of x, y and z, compares the point cloud slicing with a standard point cloud slice of the underground tunnel, and outputs position coordinates, deformation size and deformation geometric shape of tunnel deformation after the deformation is found, the deformation information can be stored, and historical information can be consulted.
The invention has the beneficial effects that:
when the software system is used, a dense 3D point cloud picture is generated based on a visual simultaneous positioning and mapping technology, the space coordinates of each point of each layer of point cloud on the surface of a roadway are obtained by utilizing a 3D point cloud slicing technology, and the deformation position, the deformation size, the deformation geometric shape and the like can be accurately obtained.
According to the invention, the surface of the underground tunnel is not required to be manually detected, and the 3D point cloud dense mapping, the point cloud slicing and the deformation detection of the surface of the underground tunnel can be completed only by carrying out autonomous movement of the whole tunnel for one to two times by the explosion-proof mobile robot with the explosion-proof binocular depth camera. Compared with manual detection, the system has the advantages of high automation degree, high detection speed and high measurement precision.
Drawings
In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art to obtain other drawings without creative efforts;
FIG. 1 is a schematic diagram of a downhole roadway surface deformation detection system of the present invention;
FIG. 2 is a schematic view of the overall structure of the explosion-proof mobile robot of the present invention;
FIG. 3 is a schematic structural view of an explosion-proof binocular depth camera of the present invention;
FIG. 4 is a schematic diagram of a software system workflow in a high performance computer;
in the figure: explosion-proof toughened glass 1, left infrared camera 2, RGB camera 3, infrared dot matrix transmitter 4, right infrared camera 5.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The underground roadway surface deformation detection system based on the 3D point cloud slice comprises an explosion-proof mobile robot, wherein an explosion-proof binocular depth camera is mounted on the explosion-proof mobile robot, the explosion-proof binocular depth camera is mounted on a vertical rod support at the front end of an automobile body of the explosion-proof mobile robot, the angle is adjustable, the explosion-proof binocular depth camera is connected with a high-performance computer through a WiFi module, and the high-performance computer comprises a software system.
The anti-explosion mobile robot consists of an anti-explosion vehicle body, a power supply, a wheel driving control system and a communication system, wherein the power supply is connected with the wheel driving control system and the communication system to realize power supply, and the anti-explosion mobile robot can independently walk or walk through remote control when in use.
Explosion-proof two mesh degree of depth cameras comprise explosion-proof toughened glass 1, RGB camera 3, infrared dot matrix transmitter 4 and two mesh infrared cameras, and two mesh infrared cameras comprise left infrared camera 2 and right infrared camera 5, and RGB camera 3 and infrared dot matrix transmitter 4 set up between left infrared camera 2 and right infrared camera 5.
After the system is started, the anti-explosion mobile robot is placed in an underground roadway, the RGB camera 3 collects RGB images in the underground roadway, and the binocular infrared camera collects depth information corresponding to RGB in a corresponding scene. The explosion-proof binocular depth camera performs one-to-two image acquisition on the whole underground tunnel, transmits video image information to a high-performance computer in real time through a WiFi module, and a software system in the high-performance computer automatically processes the acquired images based on a VSLAM algorithm to generate dense 3D point cloud images.
As shown in fig. 4, the software system of the system is based on the VSLAM algorithm, and the algorithm process includes sensor information reading, front-end visual odometer, back-end optimization, loop detection, image building, point cloud slicing and comparison.
The sensor information reading is to acquire RGB images and depth images on the surface of the underground roadway by an explosion-proof binocular depth camera;
the front-end visual odometer estimates the motion state of the camera according to the information of the front frame and the back frame of the input image data, the motion track and the local map of the camera in a short time can be obtained through the front-end visual odometer, and the back-end optimization further optimizes the established map by adopting a filter method and nonlinear optimization to obtain a more accurate 3D image;
SLAM systems are prone to track drift during camera motion, which increases over time, and loop detection is used to detect when the mobile robot has returned to the mapped area for correcting errors accumulated since the last visit to the area.
The software system builds a map according to the result, carries out through filtering on the generated dense 3D point cloud map according to three dimensionality directions of x, y and z to filter noise points, carries out voxel filtering on the result to reduce the number of point clouds, extracts a clear 3D point cloud map of the underground tunnel, automatically carries out point cloud slicing according to one dimensionality of x, y and z, compares the point cloud slice with a standard point cloud slice of the underground tunnel, and outputs results such as position coordinates of tunnel deformation, deformation size, deformation geometric shape and the like after the deformation is found, the deformation information can be stored, and historical information can be consulted.
According to the invention, the surface of the underground tunnel is not required to be manually detected, and the 3D point cloud dense mapping, the point cloud slicing and the deformation detection of the surface of the underground tunnel can be completed only by carrying out autonomous movement of the whole tunnel for one to two times by the explosion-proof mobile robot with the explosion-proof binocular depth camera. Compared with manual detection, the system has the advantages of high automation degree, high detection speed and high measurement precision.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed.
Claims (8)
1. The utility model provides a underworkings surface deformation detecting system based on 3D point cloud section, includes explosion-proof mobile robot, its characterized in that, install explosion-proof binocular depth camera on the explosion-proof mobile robot, explosion-proof binocular depth camera is connected with the high performance computer through the wiFi module, includes the software system among the high performance computer.
2. The underground roadway surface deformation detection system based on the 3D point cloud slice of claim 1, wherein the explosion-proof binocular depth camera is mounted on a vertical rod support at the front end of an explosion-proof mobile robot body, and the angle is adjustable.
3. The system for detecting the surface deformation of the underground roadway based on the 3D point cloud slice according to claim 1, wherein the explosion-proof mobile robot is composed of an explosion-proof vehicle body, a power supply, a wheel driving control system and a communication system, the power supply is connected with the wheel driving control system and the communication system to realize power supply, and the explosion-proof mobile robot can walk autonomously or remotely when in use.
4. The underground roadway surface deformation detection system based on 3D point cloud slice of claim 1, characterized in that, explosion-proof binocular depth camera comprises explosion-proof toughened glass (1), RGB camera (3), infrared dot matrix transmitter (4) and binocular infrared camera, and binocular infrared camera comprises left infrared camera (2) and right infrared camera (5), and RGB camera (3) and infrared dot matrix transmitter (4) set up between left infrared camera (2) and right infrared camera (5).
5. The system for detecting the surface deformation of the underground roadway based on the 3D point cloud slice according to claim 4, wherein the anti-explosion mobile robot is placed in the underground roadway, the RGB camera (3) collects RGB images in the underground roadway, and the binocular infrared camera collects depth information corresponding to RGB in a corresponding scene.
6. The system for detecting the surface deformation of the underground tunnel based on the 3D point cloud slice according to claim 5, wherein the explosion-proof binocular depth camera performs one to two times of image acquisition on the whole underground tunnel, transmits video image information to a high-performance computer through a WiFi module in real time, and a software system in the high-performance computer automatically processes the acquired images based on a VSLAM algorithm to generate a dense 3D point cloud image.
7. The system of claim 1, wherein the software system is based on a VSLAM algorithm, and the algorithm process comprises sensor information reading, front-end visual odometry, back-end optimization, loop detection, mapping, point cloud slicing and comparison.
8. The system according to claim 7, wherein the software system performs straight-through filtering on the generated dense 3D point cloud image in the directions of three dimensions x, y and z to filter noise points, performs voxel filtering on the result to reduce the number of point clouds, extracts a clear 3D point cloud image of the underground tunnel, automatically performs point cloud slicing in one of the dimensions x, y and z, compares the point cloud image with a standard point cloud slice of the underground tunnel, and outputs a position coordinate, a deformation size and a deformation geometry of tunnel deformation after the deformation is found, wherein the deformation information can be stored, and the history information can be consulted.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110812819.8A CN113487583A (en) | 2021-07-19 | 2021-07-19 | Underground roadway surface deformation detection system based on 3D point cloud slice |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110812819.8A CN113487583A (en) | 2021-07-19 | 2021-07-19 | Underground roadway surface deformation detection system based on 3D point cloud slice |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113487583A true CN113487583A (en) | 2021-10-08 |
Family
ID=77941204
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110812819.8A Pending CN113487583A (en) | 2021-07-19 | 2021-07-19 | Underground roadway surface deformation detection system based on 3D point cloud slice |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113487583A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115200540A (en) * | 2022-07-08 | 2022-10-18 | 安徽省皖北煤电集团有限责任公司 | Mine roadway deformation monitoring and early warning method and system |
| CN116295074A (en) * | 2023-02-13 | 2023-06-23 | 中国矿业大学 | Device and method for monitoring deformation and failure of surrounding rock in coal mine roadway based on depth image |
| CN117646828A (en) * | 2024-01-29 | 2024-03-05 | 中国市政工程西南设计研究总院有限公司 | Device and method for detecting relative displacement and water leakage of pipe jacking interface |
| CN117918882A (en) * | 2024-01-22 | 2024-04-26 | 武汉友拓科技有限公司 | Ultrasonic detector based on laser topology three-dimensional modeling and laser modeling method thereof |
| CN119579633A (en) * | 2025-02-08 | 2025-03-07 | 安徽协创物联网技术有限公司 | A 3D point cloud slice deformation detection method suitable for complex underground environments |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108345305A (en) * | 2018-01-31 | 2018-07-31 | 中国矿业大学 | Railless free-wheeled vehicle intelligent vehicle-mounted system, underground vehicle scheduling system and control method |
| CN109736894A (en) * | 2018-11-27 | 2019-05-10 | 中国矿业大学 | A monitoring system, monitoring method and early warning method for surrounding rock disaster of coal mine roadway |
| CN110941239A (en) * | 2019-12-17 | 2020-03-31 | 中国矿业大学 | A kind of deep mine environment monitoring robot system and monitoring method |
-
2021
- 2021-07-19 CN CN202110812819.8A patent/CN113487583A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108345305A (en) * | 2018-01-31 | 2018-07-31 | 中国矿业大学 | Railless free-wheeled vehicle intelligent vehicle-mounted system, underground vehicle scheduling system and control method |
| CN109736894A (en) * | 2018-11-27 | 2019-05-10 | 中国矿业大学 | A monitoring system, monitoring method and early warning method for surrounding rock disaster of coal mine roadway |
| CN110941239A (en) * | 2019-12-17 | 2020-03-31 | 中国矿业大学 | A kind of deep mine environment monitoring robot system and monitoring method |
Non-Patent Citations (2)
| Title |
|---|
| 崔胜民等: "《智能网联汽车导航定位技术》", vol. 1, 28 February 2021, 北京：人民邮电出版社, pages: 152 - 153 * |
| 殷江等: "基于激光雷达的移动机器人三维建图与定位", 《福建工程学院学报》, vol. 18, no. 04, 31 December 2020 (2020-12-31), pages 370 - 374 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115200540A (en) * | 2022-07-08 | 2022-10-18 | 安徽省皖北煤电集团有限责任公司 | Mine roadway deformation monitoring and early warning method and system |
| CN116295074A (en) * | 2023-02-13 | 2023-06-23 | 中国矿业大学 | Device and method for monitoring deformation and failure of surrounding rock in coal mine roadway based on depth image |
| CN116295074B (en) * | 2023-02-13 | 2024-05-07 | 中国矿业大学 | Coal mine tunnel surrounding rock deformation and damage monitoring device and method based on depth image |
| CN117918882A (en) * | 2024-01-22 | 2024-04-26 | 武汉友拓科技有限公司 | Ultrasonic detector based on laser topology three-dimensional modeling and laser modeling method thereof |
| CN117646828A (en) * | 2024-01-29 | 2024-03-05 | 中国市政工程西南设计研究总院有限公司 | Device and method for detecting relative displacement and water leakage of pipe jacking interface |
| CN119579633A (en) * | 2025-02-08 | 2025-03-07 | 安徽协创物联网技术有限公司 | A 3D point cloud slice deformation detection method suitable for complex underground environments |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110262546B (en) | A kind of tunnel intelligent drone inspection method | |
| CN113487583A (en) | Underground roadway surface deformation detection system based on 3D point cloud slice | |
| CN114199240B (en) | Fusion positioning system and method of two-dimensional code, lidar and IMU without GPS signal | |
| CN111551958B (en) | Mining area unmanned high-precision map manufacturing method | |
| CN109911188B (en) | Bridge detection UAV system for non-satellite navigation and positioning environment | |
| CN115407357A (en) | Low-beam LiDAR-IMU-RTK positioning and mapping algorithm based on large scenes | |
| CN103054522B (en) | A kind of cleaning robot system and investigating method thereof | |
| CN113945206A (en) | Positioning method and device based on multi-sensor fusion | |
| CN110108255B (en) | Universal mobile data acquisition and processing tunnel detection system for multiple scanners | |
| CN110412616A (en) | Method and device for acceptance inspection of underground stope in mining area | |
| CN103926933A (en) | Indoor simultaneous locating and environment modeling method for unmanned aerial vehicle | |
| CN106017463A (en) | Aircraft positioning method based on positioning and sensing device | |
| CN107990876A (en) | The quick scanning means in underground mine goaf and method based on unmanned vehicle | |
| CN110766785B (en) | Real-time positioning and three-dimensional reconstruction device and method for underground pipeline | |
| CN110456797A (en) | A AGV relocation system and method based on 2D laser sensor | |
| CN108345005A (en) | The real-time continuous autonomous positioning orientation system and navigation locating method of tunnelling machine | |
| CN106384382A (en) | Three-dimensional reconstruction system and method based on binocular stereoscopic vision | |
| CN103926927A (en) | Binocular vision positioning and three-dimensional mapping method for indoor mobile robot | |
| CN116352722A (en) | Multi-sensor fused mine inspection rescue robot and control method thereof | |
| CN109648558A (en) | Robot non-plane motion localization method and its motion locating system | |
| CN113485325A (en) | SLAM mapping and autonomous navigation method for underground coal mine water pump house inspection robot | |
| CN103400416B (en) | A kind of urban environment robot navigation method based on probability multilayer landform | |
| CN107403464A (en) | A kind of three-dimensional Mine Modeling system and method | |
| CN104369742A (en) | Image-processing-based fast intelligent detection vehicle for tunnel surface cracks | |
| CN117456108B (en) | Three-dimensional data acquisition method for line laser sensor and high-definition camera |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |