Online LiDAR-Camera Extrinsic Calibration Using Selected Semantic Features
Autonomous vehicles have gained great attention from all walks of life in recent years. The relative position and orientation between sensors often change gradually over time due to vibrations or thermal stress of materials. Thus, online re-calibrating extrinsic parameters periodically is required....
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Open Journal of Intelligent Transportation Systems |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10944781/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850188439432986624 |
|---|---|
| author | Ping-Tzu Lin Ying-Shiuan Huang Wen-Chieh Lin Chieh-Chih Wang Huei-Yung Lin |
| author_facet | Ping-Tzu Lin Ying-Shiuan Huang Wen-Chieh Lin Chieh-Chih Wang Huei-Yung Lin |
| author_sort | Ping-Tzu Lin |
| collection | DOAJ |
| description | Autonomous vehicles have gained great attention from all walks of life in recent years. The relative position and orientation between sensors often change gradually over time due to vibrations or thermal stress of materials. Thus, online re-calibrating extrinsic parameters periodically is required. In this situation, automatic targetless methods are more preferable as they do not require a calibration target or tedious calibration procedure. In this paper, we propose an online targetless camera-LiDAR extrinsic calibration approach with the help of semantic information. Our method could effectively ameliorate the problem of targetless methods which usually lack robust features and the correspondences. We also propose a feature selection technique to filter out improper feature correspondences by matching the image contours and point cloud projection contours. The experiment results show that our approach is more robust than previous work, and the calibration algorithm is applicable to more scenarios. |
| format | Article |
| id | doaj-art-abeba6ec86004b498a4f9044ec3b217b |
| institution | OA Journals |
| issn | 2687-7813 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Intelligent Transportation Systems |
| spelling | doaj-art-abeba6ec86004b498a4f9044ec3b217b2025-08-20T02:15:53ZengIEEEIEEE Open Journal of Intelligent Transportation Systems2687-78132025-01-01645646410.1109/OJITS.2025.355557410944781Online LiDAR-Camera Extrinsic Calibration Using Selected Semantic FeaturesPing-Tzu Lin0Ying-Shiuan Huang1Wen-Chieh Lin2https://orcid.org/0000-0002-9704-5373Chieh-Chih Wang3https://orcid.org/0000-0001-9385-0044Huei-Yung Lin4https://orcid.org/0000-0002-6476-6625College of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, TaiwanCollege of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, TaiwanCollege of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, TaiwanDepartment of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu, TaiwanDepartment of Computer Science and Information Engineering, National Taipei University of Technology, Taipei, TaiwanAutonomous vehicles have gained great attention from all walks of life in recent years. The relative position and orientation between sensors often change gradually over time due to vibrations or thermal stress of materials. Thus, online re-calibrating extrinsic parameters periodically is required. In this situation, automatic targetless methods are more preferable as they do not require a calibration target or tedious calibration procedure. In this paper, we propose an online targetless camera-LiDAR extrinsic calibration approach with the help of semantic information. Our method could effectively ameliorate the problem of targetless methods which usually lack robust features and the correspondences. We also propose a feature selection technique to filter out improper feature correspondences by matching the image contours and point cloud projection contours. The experiment results show that our approach is more robust than previous work, and the calibration algorithm is applicable to more scenarios.https://ieeexplore.ieee.org/document/10944781/CameraLiDARextrinsic calibrationtargetlesssemantic features |
| spellingShingle | Ping-Tzu Lin Ying-Shiuan Huang Wen-Chieh Lin Chieh-Chih Wang Huei-Yung Lin Online LiDAR-Camera Extrinsic Calibration Using Selected Semantic Features IEEE Open Journal of Intelligent Transportation Systems Camera LiDAR extrinsic calibration targetless semantic features |
| title | Online LiDAR-Camera Extrinsic Calibration Using Selected Semantic Features |
| title_full | Online LiDAR-Camera Extrinsic Calibration Using Selected Semantic Features |
| title_fullStr | Online LiDAR-Camera Extrinsic Calibration Using Selected Semantic Features |
| title_full_unstemmed | Online LiDAR-Camera Extrinsic Calibration Using Selected Semantic Features |
| title_short | Online LiDAR-Camera Extrinsic Calibration Using Selected Semantic Features |
| title_sort | online lidar camera extrinsic calibration using selected semantic features |
| topic | Camera LiDAR extrinsic calibration targetless semantic features |
| url | https://ieeexplore.ieee.org/document/10944781/ |
| work_keys_str_mv | AT pingtzulin onlinelidarcameraextrinsiccalibrationusingselectedsemanticfeatures AT yingshiuanhuang onlinelidarcameraextrinsiccalibrationusingselectedsemanticfeatures AT wenchiehlin onlinelidarcameraextrinsiccalibrationusingselectedsemanticfeatures AT chiehchihwang onlinelidarcameraextrinsiccalibrationusingselectedsemanticfeatures AT hueiyunglin onlinelidarcameraextrinsiccalibrationusingselectedsemanticfeatures |