Online Camera-LiDAR Calibration Monitoring and Rotational Drift Tracking

Abstract

The relative poses of visual perception sensors distributed over a vehicle's body may vary due to dynamic forces, thermal dilations, or minor accidents. This paper proposes two methods, OCAMO and LTO, that monitor and track the LiDAR-Camera extrinsic calibration parameters online. Calibration monitoring provides a certificate for reference calibration parameters validity. Tracking follows the calibration parameters drift in time. OCAMO is based on an adaptive online stochastic optimization with a memory of past evolution. LTO uses a fixed-grid search for the optimal parameters per frame and without memory. Both methods use low-level point-like features and a robust kernel-based loss function and work with a small memory footprint and computational overhead. Both include a preselection of informative data that limits their divergence. The statistical accuracy of both calibration monitoring methods is over 98%, whereas OCAMO monitoring can detect small decalibrations better, and LTO monitoring reacts faster on abrupt decalibrations. The tracking variants of both methods follow random calibration drift with an accuracy of about 0.03° in the yaw angle.