Robust Parking Space Recognition Approach Based on Tightly Coupled Polarized Lidar and Pre-Integration IMU
Improving the accuracy of parking space recognition is crucial in the fields for Automated Valet Parking (AVP) of autonomous driving. In AVP, accurate free space recognition significantly impacts the safety and comfort of both the vehicles and drivers. To enhance parking space recognition and annota...
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MDPI AG
2024-10-01
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| author | Jialiang Chen Fei Li Xiaohui Liu Yuelin Yuan |
| author_facet | Jialiang Chen Fei Li Xiaohui Liu Yuelin Yuan |
| author_sort | Jialiang Chen |
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| description | Improving the accuracy of parking space recognition is crucial in the fields for Automated Valet Parking (AVP) of autonomous driving. In AVP, accurate free space recognition significantly impacts the safety and comfort of both the vehicles and drivers. To enhance parking space recognition and annotation in unknown environments, this paper proposes an automatic parking space annotation approach with tight coupling of Lidar and Inertial Measurement Unit (IMU). First, the pose of the Lidar frame was tightly coupled with high-frequency IMU data to compensate for vehicle motion, reducing its impact on the pose transformation of the Lidar point cloud. Next, simultaneous localization and mapping (SLAM) were performed using the compensated Lidar frame. By extracting two-dimensional polarized edge features and planar features from the three-dimensional Lidar point cloud, a polarized Lidar odometry was constructed. The polarized Lidar odometry factor and loop closure factor were jointly optimized in the iSAM2. Finally, the pitch angle of the constructed local map was evaluated to filter out ground points, and the regions of interest (ROI) were projected onto a grid map. The free space between adjacent vehicle point clouds was assessed on the grid map using convex hull detection and straight-line fitting. The experiments were conducted on both local and open datasets. The proposed method achieved an average precision and recall of 98.89% and 98.79% on the local dataset, respectively; it also achieved 97.08% and 99.40% on the nuScenes dataset. And it reduced storage usage by 48.38% while ensuring running time. Comparative experiments on open datasets show that the proposed method can adapt to various scenarios and exhibits strong robustness. |
| format | Article |
| id | doaj-art-2ef6e3cbd2d843e4a403ec42df0f8769 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-2ef6e3cbd2d843e4a403ec42df0f87692025-08-20T02:10:57ZengMDPI AGApplied Sciences2076-34172024-10-011420918110.3390/app14209181Robust Parking Space Recognition Approach Based on Tightly Coupled Polarized Lidar and Pre-Integration IMUJialiang Chen0Fei Li1Xiaohui Liu2Yuelin Yuan3Department of Transportation and Logistics, Dalian University of Technology, Dalian 116024, ChinaCollege of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, ChinaCollege of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, ChinaCollege of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, ChinaImproving the accuracy of parking space recognition is crucial in the fields for Automated Valet Parking (AVP) of autonomous driving. In AVP, accurate free space recognition significantly impacts the safety and comfort of both the vehicles and drivers. To enhance parking space recognition and annotation in unknown environments, this paper proposes an automatic parking space annotation approach with tight coupling of Lidar and Inertial Measurement Unit (IMU). First, the pose of the Lidar frame was tightly coupled with high-frequency IMU data to compensate for vehicle motion, reducing its impact on the pose transformation of the Lidar point cloud. Next, simultaneous localization and mapping (SLAM) were performed using the compensated Lidar frame. By extracting two-dimensional polarized edge features and planar features from the three-dimensional Lidar point cloud, a polarized Lidar odometry was constructed. The polarized Lidar odometry factor and loop closure factor were jointly optimized in the iSAM2. Finally, the pitch angle of the constructed local map was evaluated to filter out ground points, and the regions of interest (ROI) were projected onto a grid map. The free space between adjacent vehicle point clouds was assessed on the grid map using convex hull detection and straight-line fitting. The experiments were conducted on both local and open datasets. The proposed method achieved an average precision and recall of 98.89% and 98.79% on the local dataset, respectively; it also achieved 97.08% and 99.40% on the nuScenes dataset. And it reduced storage usage by 48.38% while ensuring running time. Comparative experiments on open datasets show that the proposed method can adapt to various scenarios and exhibits strong robustness.https://www.mdpi.com/2076-3417/14/20/9181autonomous vehiclesfree space recognitiontightly coupled SLAMpolarized Lidar odometryparking space annotation |
| spellingShingle | Jialiang Chen Fei Li Xiaohui Liu Yuelin Yuan Robust Parking Space Recognition Approach Based on Tightly Coupled Polarized Lidar and Pre-Integration IMU Applied Sciences autonomous vehicles free space recognition tightly coupled SLAM polarized Lidar odometry parking space annotation |
| title | Robust Parking Space Recognition Approach Based on Tightly Coupled Polarized Lidar and Pre-Integration IMU |
| title_full | Robust Parking Space Recognition Approach Based on Tightly Coupled Polarized Lidar and Pre-Integration IMU |
| title_fullStr | Robust Parking Space Recognition Approach Based on Tightly Coupled Polarized Lidar and Pre-Integration IMU |
| title_full_unstemmed | Robust Parking Space Recognition Approach Based on Tightly Coupled Polarized Lidar and Pre-Integration IMU |
| title_short | Robust Parking Space Recognition Approach Based on Tightly Coupled Polarized Lidar and Pre-Integration IMU |
| title_sort | robust parking space recognition approach based on tightly coupled polarized lidar and pre integration imu |
| topic | autonomous vehicles free space recognition tightly coupled SLAM polarized Lidar odometry parking space annotation |
| url | https://www.mdpi.com/2076-3417/14/20/9181 |
| work_keys_str_mv | AT jialiangchen robustparkingspacerecognitionapproachbasedontightlycoupledpolarizedlidarandpreintegrationimu AT feili robustparkingspacerecognitionapproachbasedontightlycoupledpolarizedlidarandpreintegrationimu AT xiaohuiliu robustparkingspacerecognitionapproachbasedontightlycoupledpolarizedlidarandpreintegrationimu AT yuelinyuan robustparkingspacerecognitionapproachbasedontightlycoupledpolarizedlidarandpreintegrationimu |