ArticleSensors (Basel, Switzerland)2017
LiDAR-IMU Time Delay Calibration Based on Iterative Closest Point and Iterated Sigma Point Kalman Filter.
Article in Sensors (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A Scheme for System Error Calibration and Compensation of the Initial State of MEMS Inertial Navigation.Sensors (Basel, Switzerland) · 2025Article
- A Point Cloud Data-Driven Pallet Pose Estimation Method Using an Active Binocular Vision Sensor.Sensors (Basel, Switzerland) · 2023Article
- OMC-SLIO: Online Multiple Calibrations Spinning LiDAR Inertial Odometry.Sensors (Basel, Switzerland) · 2022Article
- MEMS Inertial Sensor Calibration Technology: Current Status and Future Trends.Micromachines · 2022Review
- A Pedestrian Detection Algorithm Based on Score Fusion for Multi-LiDAR Systems.Sensors (Basel, Switzerland) · 2021Article
- LiDAR-IMU Time Delay Calibration Based on Iterative Closest Point and Iterated Sigma Point Kalman Filter. Sensors 2017, 17, 539.Sensors (Basel, Switzerland) · 2017Article
Corrections and comments
- Erratum issued
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The time delay calibration between Light Detection and Ranging (LiDAR) and Inertial Measurement Units (IMUs) is an essential prerequisite for its applications. However, the correspondences between LiDAR and IMU measurements are usually unknown, and thus cannot be computed directly for the time delay calibration. In order to solve the problem of LiDAR-IMU time delay calibration, this paper presents a fusion method based on iterative closest point (ICP) and iterated sigma point Kalman filter (ISPKF), which combines the advantages of ICP and ISPKF. The ICP algorithm can precisely determine the unknown transformation between LiDAR-IMU; and the ISPKF algorithm can optimally estimate the time delay calibration parameters. First of all, the coordinate transformation from the LiDAR frame to the IMU frame is realized. Second, the measurement model and time delay error model of LiDAR and IMU are established. Third, the methodology of the ICP and ISPKF procedure is presented for LiDAR-IMU time delay calibration. Experimental results are presented that validate the proposed method and demonstrate the time delay error can be accurately calibrated.
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