LiDAR and IMU Tightly Coupled Localization System Based on Ground Constraint in Flat Scenario
Accurate estimation of current position and attitude of a vehicle is one of the key technologies for autonomous driving. Due to the defect of LiDAR intrinsic parameter and the sparsity of LiDAR beam in the vertical direction, current LiDAR-based simultaneous localization and mapping (SLAM) system ge...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Open Journal of Intelligent Transportation Systems |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10540251/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832590368395231232 |
|---|---|
| author | Man Yu Keyang Gong Weihua Zhao Rui Liu |
| author_facet | Man Yu Keyang Gong Weihua Zhao Rui Liu |
| author_sort | Man Yu |
| collection | DOAJ |
| description | Accurate estimation of current position and attitude of a vehicle is one of the key technologies for autonomous driving. Due to the defect of LiDAR intrinsic parameter and the sparsity of LiDAR beam in the vertical direction, current LiDAR-based simultaneous localization and mapping (SLAM) system generally suffers from the problem of inaccurate height positioning. In this study, a LiDAR and inertial measurement unit (IMU) tightly coupled localization algorithm considering ground constraint is proposed, which is developed based on a pose graph optimization framework. At the front end, the ground segmentation algorithm Patchwork is improved to obtain a point cloud with higher verticality, which is added to the LiDAR inertial odometry. Moreover, constraints are constructed by using current frame ground points and world map ground points, which are added to factor map optimization to limit elevation errors. At the back end, SC++ descriptors are used to construct loop constraints to eliminate accumulated errors. Verifications based on KITTI dataset show that the height positioning accuracy will be improved through introducing ground constraint factor and loop detection factor. Real vehicle tests indicate that the proposed algorithm has better height positioning accuracy and better robustness compared with the LeGO-LOAM algorithm. |
| format | Article |
| id | doaj-art-f82ff83e50274d5794db650616b0f66e |
| institution | Kabale University |
| issn | 2687-7813 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Intelligent Transportation Systems |
| spelling | doaj-art-f82ff83e50274d5794db650616b0f66e2025-01-24T00:02:48ZengIEEEIEEE Open Journal of Intelligent Transportation Systems2687-78132024-01-01529630610.1109/OJITS.2024.340639010540251LiDAR and IMU Tightly Coupled Localization System Based on Ground Constraint in Flat ScenarioMan Yu0Keyang Gong1Weihua Zhao2Rui Liu3https://orcid.org/0000-0001-6071-5225School of Construction Machinery, Chang’an University, Xi’an, ChinaIntelligent Driving Center, Geely Automotive Research Institute (Ningbo) Company, Ningbo, ChinaSchool of Vehicle Engineering, Xi’an Aeronautical Institute, Xi’an, ChinaSchool of Automobile, Chang’an University, Xi’an, ChinaAccurate estimation of current position and attitude of a vehicle is one of the key technologies for autonomous driving. Due to the defect of LiDAR intrinsic parameter and the sparsity of LiDAR beam in the vertical direction, current LiDAR-based simultaneous localization and mapping (SLAM) system generally suffers from the problem of inaccurate height positioning. In this study, a LiDAR and inertial measurement unit (IMU) tightly coupled localization algorithm considering ground constraint is proposed, which is developed based on a pose graph optimization framework. At the front end, the ground segmentation algorithm Patchwork is improved to obtain a point cloud with higher verticality, which is added to the LiDAR inertial odometry. Moreover, constraints are constructed by using current frame ground points and world map ground points, which are added to factor map optimization to limit elevation errors. At the back end, SC++ descriptors are used to construct loop constraints to eliminate accumulated errors. Verifications based on KITTI dataset show that the height positioning accuracy will be improved through introducing ground constraint factor and loop detection factor. Real vehicle tests indicate that the proposed algorithm has better height positioning accuracy and better robustness compared with the LeGO-LOAM algorithm.https://ieeexplore.ieee.org/document/10540251/LiDAR inertial systemheight positioningpose graph optimizationground constraint |
| spellingShingle | Man Yu Keyang Gong Weihua Zhao Rui Liu LiDAR and IMU Tightly Coupled Localization System Based on Ground Constraint in Flat Scenario IEEE Open Journal of Intelligent Transportation Systems LiDAR inertial system height positioning pose graph optimization ground constraint |
| title | LiDAR and IMU Tightly Coupled Localization System Based on Ground Constraint in Flat Scenario |
| title_full | LiDAR and IMU Tightly Coupled Localization System Based on Ground Constraint in Flat Scenario |
| title_fullStr | LiDAR and IMU Tightly Coupled Localization System Based on Ground Constraint in Flat Scenario |
| title_full_unstemmed | LiDAR and IMU Tightly Coupled Localization System Based on Ground Constraint in Flat Scenario |
| title_short | LiDAR and IMU Tightly Coupled Localization System Based on Ground Constraint in Flat Scenario |
| title_sort | lidar and imu tightly coupled localization system based on ground constraint in flat scenario |
| topic | LiDAR inertial system height positioning pose graph optimization ground constraint |
| url | https://ieeexplore.ieee.org/document/10540251/ |
| work_keys_str_mv | AT manyu lidarandimutightlycoupledlocalizationsystembasedongroundconstraintinflatscenario AT keyanggong lidarandimutightlycoupledlocalizationsystembasedongroundconstraintinflatscenario AT weihuazhao lidarandimutightlycoupledlocalizationsystembasedongroundconstraintinflatscenario AT ruiliu lidarandimutightlycoupledlocalizationsystembasedongroundconstraintinflatscenario |