From Chaos to Calibration: A Geometric Mutual Information Approach to Target-Free Camera LiDAR Extrinsic Calibration
This addresses the challenge of scalable sensor calibration for autonomous systems, offering a practical solution that is incremental by building on earlier mutual information methods.
The paper tackles the problem of target-free extrinsic calibration between cameras and LiDARs for autonomous vehicles, proposing a geometric mutual information approach that achieves accurate and robust calibration without requiring ground truth data or constrained setups.
Sensor fusion is vital for the safe and robust operation of autonomous vehicles. Accurate extrinsic sensor to sensor calibration is necessary to accurately fuse multiple sensor's data in a common spatial reference frame. In this paper, we propose a target free extrinsic calibration algorithm that requires no ground truth training data, artificially constrained motion trajectories, hand engineered features or offline optimization and that is accurate, precise and extremely robust to initialization error. Most current research on online camera-LiDAR extrinsic calibration requires ground truth training data which is impossible to capture at scale. We revisit analytical mutual information based methods first proposed in 2012 and demonstrate that geometric features provide a robust information metric for camera-LiDAR extrinsic calibration. We demonstrate our proposed improvement using the KITTI and KITTI-360 fisheye data set.