Research on unmanned vehicle obstacle avoidance technology based on LIDAR and depth camera fusion
To address the problems of poor accuracy of traditional EKF algorithm in estimating the position of unmanned vehicles and the deficiencies in accuracy and map completeness of the traditional map building method with single-line LiDAR, this paper proposes a method to create fused raster maps realized...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Sciendo
2024-01-01
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| Series: | Applied Mathematics and Nonlinear Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/amns.2023.2.00575 |
| _version_ | 1797340727810719744 |
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| author | Qiu Hao Chen Weifeng Ji Aihong Hu Kai |
| author_facet | Qiu Hao Chen Weifeng Ji Aihong Hu Kai |
| author_sort | Qiu Hao |
| collection | DOAJ |
| description | To address the problems of poor accuracy of traditional EKF algorithm in estimating the position of unmanned vehicles and the deficiencies in accuracy and map completeness of the traditional map building method with single-line LiDAR, this paper proposes a method to create fused raster maps realized with multi-source data. Firstly, the combined data of the inertial measurement unit and wheel encoder are corrected by adding the positional information output from the visual odometer using the error-state SLAM algorithm, and the local raster constructed by LiDAR and depth camera is fused frame by frame using the idea of Bayesian estimation to finally generate the fused global map. Then, a four-wheeled mobile unmanned vehicle with a LiDAR sensor and depth camera is selected as the experimental object, and dynamic environment avoidance simulation experiments are conducted to draw conclusions. The simulation experiment results show that when γ = 5.99, the algorithm generates a new local target point pg2 (17.49, 13.49) and the corresponding getaway path and finally guides the unmanned vehicle to the specified target point, verifying that the method in this paper can achieve the avoidance capability of the unmanned vehicle in the process of getting away from the newly emerged obstacles. This study uses the scanned data of LiDAR for the estimation of the real-time position of the unmanned vehicle to realize obstacle avoidance and path planning of the unmanned vehicle. |
| first_indexed | 2024-03-08T10:07:27Z |
| format | Article |
| id | doaj.art-1d777dc83f704d65957c5482a4970aa1 |
| institution | Directory Open Access Journal |
| issn | 2444-8656 |
| language | English |
| last_indexed | 2024-03-08T10:07:27Z |
| publishDate | 2024-01-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Applied Mathematics and Nonlinear Sciences |
| spelling | doaj.art-1d777dc83f704d65957c5482a4970aa12024-01-29T08:52:34ZengSciendoApplied Mathematics and Nonlinear Sciences2444-86562024-01-019110.2478/amns.2023.2.00575Research on unmanned vehicle obstacle avoidance technology based on LIDAR and depth camera fusionQiu Hao0Chen Weifeng1Ji Aihong2Hu Kai31School of Automation, Nanjing University of Information Science & Technology, Nanjing, Jiangsu, 210044, China.1School of Automation, Nanjing University of Information Science & Technology, Nanjing, Jiangsu, 210044, China.3Lab of Locomotion Bioinspiration and Intelligent Robots, College of Mechanical & Electrical Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing, Jiangsu, 210016, China.1School of Automation, Nanjing University of Information Science & Technology, Nanjing, Jiangsu, 210044, China.To address the problems of poor accuracy of traditional EKF algorithm in estimating the position of unmanned vehicles and the deficiencies in accuracy and map completeness of the traditional map building method with single-line LiDAR, this paper proposes a method to create fused raster maps realized with multi-source data. Firstly, the combined data of the inertial measurement unit and wheel encoder are corrected by adding the positional information output from the visual odometer using the error-state SLAM algorithm, and the local raster constructed by LiDAR and depth camera is fused frame by frame using the idea of Bayesian estimation to finally generate the fused global map. Then, a four-wheeled mobile unmanned vehicle with a LiDAR sensor and depth camera is selected as the experimental object, and dynamic environment avoidance simulation experiments are conducted to draw conclusions. The simulation experiment results show that when γ = 5.99, the algorithm generates a new local target point pg2 (17.49, 13.49) and the corresponding getaway path and finally guides the unmanned vehicle to the specified target point, verifying that the method in this paper can achieve the avoidance capability of the unmanned vehicle in the process of getting away from the newly emerged obstacles. This study uses the scanned data of LiDAR for the estimation of the real-time position of the unmanned vehicle to realize obstacle avoidance and path planning of the unmanned vehicle.https://doi.org/10.2478/amns.2023.2.00575lidardepth cameraraster map methodunmanned vehicleobstacle avoidance techniquepositional attitudebayesian estimation78-02 |
| spellingShingle | Qiu Hao Chen Weifeng Ji Aihong Hu Kai Research on unmanned vehicle obstacle avoidance technology based on LIDAR and depth camera fusion Applied Mathematics and Nonlinear Sciences lidar depth camera raster map method unmanned vehicle obstacle avoidance technique positional attitude bayesian estimation 78-02 |
| title | Research on unmanned vehicle obstacle avoidance technology based on LIDAR and depth camera fusion |
| title_full | Research on unmanned vehicle obstacle avoidance technology based on LIDAR and depth camera fusion |
| title_fullStr | Research on unmanned vehicle obstacle avoidance technology based on LIDAR and depth camera fusion |
| title_full_unstemmed | Research on unmanned vehicle obstacle avoidance technology based on LIDAR and depth camera fusion |
| title_short | Research on unmanned vehicle obstacle avoidance technology based on LIDAR and depth camera fusion |
| title_sort | research on unmanned vehicle obstacle avoidance technology based on lidar and depth camera fusion |
| topic | lidar depth camera raster map method unmanned vehicle obstacle avoidance technique positional attitude bayesian estimation 78-02 |
| url | https://doi.org/10.2478/amns.2023.2.00575 |
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