RobustCalib: Robust Lidar-Camera Extrinsic Calibration with Consistency Learning

Understand

Current traditional methods for LiDAR-camera extrinsics estimation depend on offline targets and human efforts, while learning-based approaches resort to iterative refinement for calibration results, posing constraints on their generalization and application in on-board systems.

• In this paper, we propose a novel approach to address the extrinsic calibration problem in a robust, automatic, and single-shot manner.
• Instead of directly optimizing extrinsics, we leverage the consistency learning between LiDAR and camera to implement implicit re-calibartion.
• Specially, we introduce an appearance-consistency loss and a geometric-consistency loss to minimizing the inconsitency between the attrbutes (e.g., intensity and depth) of projected LiDAR points and the predicted ones.