One target to align them all: LiDAR, RGB and event cameras extrinsic calibration for Autonomous Driving
This addresses the critical need for precise multi-sensor alignment in autonomous driving systems, offering an incremental improvement over existing pairwise calibration methods.
The paper tackles the problem of extrinsic calibration for LiDAR, RGB, and event cameras in autonomous driving by introducing a novel 3D calibration target that enables one-shot joint calibration, achieving accurate and robust results as validated on a custom dataset.
We present a novel multi-modal extrinsic calibration framework designed to simultaneously estimate the relative poses between event cameras, LiDARs, and RGB cameras, with particular focus on the challenging event camera calibration. Core of our approach is a novel 3D calibration target, specifically designed and constructed to be concurrently perceived by all three sensing modalities. The target encodes features in planes, ChArUco, and active LED patterns, each tailored to the unique characteristics of LiDARs, RGB cameras, and event cameras respectively. This unique design enables a one-shot, joint extrinsic calibration process, in contrast to existing approaches that typically rely on separate, pairwise calibrations. Our calibration pipeline is designed to accurately calibrate complex vision systems in the context of autonomous driving, where precise multi-sensor alignment is critical. We validate our approach through an extensive experimental evaluation on a custom built dataset, recorded with an advanced autonomous driving sensor setup, confirming the accuracy and robustness of our method.