期刊
IEEE ACCESS
卷 9, 期 -, 页码 58102-58115出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3072935
关键词
Laser radar; Simultaneous localization and mapping; Vibrations; Three-dimensional displays; Interference; Location awareness; Feature extraction; LiDAR SLAM; external disturbance; static drift; Kalman filter; parameter estimation; pose compensation
资金
- National Natural Science Foundation of China [52072020]
- Beijing Natural Science Foundation [L191002]
This study proposes a new strategy to reduce static drift at a local scale by identifying and compensating the influence of external disturbances based on the localization results of LiDAR SLAM. Through contrast experiments, potential inducing factors of static drift were analyzed, and a Kalman filter was used to estimate speed and acceleration parameters to establish an estimation criterion of static drift. Subsequently, a static drift compensation method for LiDAR SLAM was proposed, achieving a significant reduction in final positioning error.
With the extending use of LiDAR SLAM in various areas, the interference of external disturbances on SLAM is becoming more and more obvious. Huge efforts have been made to reduce the drift error of LiDAR SLAM using graph-based methods. However, the mapping results can be severely affected by external disturbances under extreme conditions, which will limit the performance of graph-based methods. This study proposes a new strategy to reduce the static drift on a local scale by identifying and compensating the influence of external disturbances based on the localization results of LiDAR SLAM. Contrast experiments were first designed and performed to analyze the potential inducing factors of static drift, such as environment and vibration. The Kalman filter was adopted to estimate the speed and acceleration parameters based on the localization results of LiDAR SLAM. Then, an estimation criterion of static drift was established according to the interference of external disturbances on speed and acceleration. Finally, a static drift compensation method for LiDAR SLAM was proposed to compensate the drift of the pose. In the verification experiment, for 1866 data points, the identification accuracy of static drift was 97.32%, and the final positioning error of LiDAR SLAM was reduced from 4.9464 m to 0.1741 m after the compensation of static drift.
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