Online Triangular Constraint Calibration for LiDAR and Cameras in Open-Pit Mines

Extrinsic calibration between a LiDAR and a camera is a crucial task for intelligent vehicles and mobile robots. However, most of the current extrinsic calibration methods are offline and target-based, making it hard to achieve real-time calibration depending on the characteristics of the environments. Although a few online calibration strategies can solve the above drawbacks, they still rely on a large amount of labeled data and struggle to achieve satisfactory performance in extrinsic calibration in unstructured scenarios where valid targets are scarce. In this work, we propose an effective online calibration strategy for mining scenarios, one of the typical unstructured scenarios. Based on the analysis of mining area data, we adopt an approach that integrates image and point cloud segmentation to accomplish the entire calibration process. An initialization method supporting a single rigid target is proposed, effectively addressing the initialization failure caused by the scarcity of targets in mining areas. Incorporating the segmentation results from the right camera to establish a left-right camera-LiDAR triangular constraint effectively reduces translation errors. Temporal data with confidence scores are then used to update and optimize the extrinsic parameters. Our online extrinsic calibration method has been validated on mining scenarios data and has achieved less than 0.2 m in data collected from an SUV and 0.3 m from trucks in translation errors within 10 frames. Besides, we provide a detailed discussion on the challenges of calibration in mining areas and the limitations of the proposed method.