Online Calibration of LiDAR and GPS/INS Using Multi-Feature Adaptive Optimization in Unstructured Environments

Accurate extrinsic calibration of parameters between LiDAR and GPS/INS is crucial for sensor fusion and simultaneous localization and mapping (SLAM) in autonomous driving systems. A drift in calibrated extrinsic parameters during operation can lead to decreased accuracy and compromised safety. In order to address this challenge, we introduce an online calibration method for LiDAR and GPS/INS parameters using multi-feature adaptive optimization. Multiple features, such as ground planes, pillars, and vertices, are extracted from straight and curved trajectories, respectively. The features from adjacent frames are used to construct loss functions based on point-to-plane distance, point-to-line distance, and nearest neighbor lateral distance principles. Furthermore, we perform a theoretical analysis of the influence of different features on calibration parameters. Based on the analysis, the effective data segments are selected to avoid the unnecessary computational burden of processing all data indiscriminately. Additionally, adaptive weights are applied to combine different features to achieve high-precision calibration results. The proposed method was validated with KITTI datasets and real-world open-pit coal mine datasets and compared with state-of-the-art (SOTA) methods. Experimental results indicate that our method effectively corrects the extrinsic calibration between LiDAR and GPS/INS online, achieving an average relative rotation error (RRE) and relative translation error (RTE) improved by 6.4% and 8.5%, respectively, compared to SOTA approaches. Moreover, the optimization time of our method is less than 13 s, with a maximum time efficiency improvement of 25.5% compared to the SOTA methods.