Continuous Online Extrinsic Calibration of Fisheye Camera and LiDAR
This addresses the need for continuous online calibration in automated driving systems to prevent errors from physical disturbances, though it is incremental as it builds on existing depth estimation networks.
The paper tackled the problem of maintaining accurate extrinsic calibration between a fisheye camera and LiDAR over a vehicle's lifetime, proposing a method that uses mutual information between camera depth estimates and LiDAR geometric distances for optimization, achieving demonstrated accuracy, precision, speed, and self-diagnosis on the KITTI-360 dataset.
Automated driving systems use multi-modal sensor suites to ensure the reliable, redundant and robust perception of the operating domain, for example camera and LiDAR. An accurate extrinsic calibration is required to fuse the camera and LiDAR data into a common spatial reference frame required by high-level perception functions. Over the life of the vehicle the value of the extrinsic calibration can change due physical disturbances, introducing an error into the high-level perception functions. Therefore there is a need for continuous online extrinsic calibration algorithms which can automatically update the value of the camera-LiDAR calibration during the life of the vehicle using only sensor data. We propose using mutual information between the camera image's depth estimate, provided by commonly available monocular depth estimation networks, and the LiDAR pointcloud's geometric distance as a optimization metric for extrinsic calibration. Our method requires no calibration target, no ground truth training data and no expensive offline optimization. We demonstrate our algorithm's accuracy, precision, speed and self-diagnosis capability on the KITTI-360 data set.