Calibration of LiDARs with object detection method
Multi - LiDAR calibration, or point cloud registration method is an important subject in the field of 3D target recognition and automatic driving. The objective of this task is to find the transformation matrix from target point cloud to source point cloud, so as to transform point clouds in differe...
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| Format: | Thesis-Master by Coursework |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/158491 |
| _version_ | 1826128107051941888 |
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| author | Gao, Jingtong |
| author2 | Wang Dan Wei |
| author_facet | Wang Dan Wei Gao, Jingtong |
| author_sort | Gao, Jingtong |
| collection | NTU |
| description | Multi - LiDAR calibration, or point cloud registration method is an important subject in the field of 3D target recognition and automatic driving. The objective of this task is to find the transformation matrix from target point cloud to source point cloud, so as to transform point clouds in different coordinate systems to the same coordinate system.
At present, matching methods of point cloud mostly use the whole characteristics of point clouds to find the transformation matrix. However, these methods assume a high degree of geometric similarity between two point clouds. Therefore, these methods are only applicable to short baseline scenarios where point clouds are obtained from LiDARs with short distance and similar pose. For large baseline scenarios where point clouds are taken from LiDARs at a large distance with a large viewpoint difference, these methods can not get good calibration results.
This dissertation propose a new LiDAR calibration method Object4Calib++ for large baseline scenarios using bounding box features. It also applies to point clouds generated by other sensors. This method greatly reduces the dependence on the similarity of point clouds and greatly increases the applicable distance of remote point clouds with incomplete similarity. Therefore, it has great application value in practical scenes. Meanwhile, this dissertation also carries out real world and simulation experiments to verify that the accuracy of our method can meet the actual use requirements. What's more, this dissertation also provides a super-large baseline calibration method CityScaleCalib based on Object4Calib++ and verifies its feasibility in Gazebo simulation environment. |
| first_indexed | 2024-10-01T07:19:31Z |
| format | Thesis-Master by Coursework |
| id | ntu-10356/158491 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:19:31Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1584912023-07-04T17:44:44Z Calibration of LiDARs with object detection method Gao, Jingtong Wang Dan Wei School of Electrical and Electronic Engineering EDWWANG@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Engineering::Electrical and electronic engineering::Control and instrumentation::Robotics Multi - LiDAR calibration, or point cloud registration method is an important subject in the field of 3D target recognition and automatic driving. The objective of this task is to find the transformation matrix from target point cloud to source point cloud, so as to transform point clouds in different coordinate systems to the same coordinate system. At present, matching methods of point cloud mostly use the whole characteristics of point clouds to find the transformation matrix. However, these methods assume a high degree of geometric similarity between two point clouds. Therefore, these methods are only applicable to short baseline scenarios where point clouds are obtained from LiDARs with short distance and similar pose. For large baseline scenarios where point clouds are taken from LiDARs at a large distance with a large viewpoint difference, these methods can not get good calibration results. This dissertation propose a new LiDAR calibration method Object4Calib++ for large baseline scenarios using bounding box features. It also applies to point clouds generated by other sensors. This method greatly reduces the dependence on the similarity of point clouds and greatly increases the applicable distance of remote point clouds with incomplete similarity. Therefore, it has great application value in practical scenes. Meanwhile, this dissertation also carries out real world and simulation experiments to verify that the accuracy of our method can meet the actual use requirements. What's more, this dissertation also provides a super-large baseline calibration method CityScaleCalib based on Object4Calib++ and verifies its feasibility in Gazebo simulation environment. Master of Science (Computer Control and Automation) 2022-05-26T01:12:30Z 2022-05-26T01:12:30Z 2022 Thesis-Master by Coursework Gao, J. (2022). Calibration of LiDARs with object detection method. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158491 https://hdl.handle.net/10356/158491 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Engineering::Electrical and electronic engineering::Control and instrumentation::Robotics Gao, Jingtong Calibration of LiDARs with object detection method |
| title | Calibration of LiDARs with object detection method |
| title_full | Calibration of LiDARs with object detection method |
| title_fullStr | Calibration of LiDARs with object detection method |
| title_full_unstemmed | Calibration of LiDARs with object detection method |
| title_short | Calibration of LiDARs with object detection method |
| title_sort | calibration of lidars with object detection method |
| topic | Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Engineering::Electrical and electronic engineering::Control and instrumentation::Robotics |
| url | https://hdl.handle.net/10356/158491 |
| work_keys_str_mv | AT gaojingtong calibrationoflidarswithobjectdetectionmethod |