Continuous Online Extrinsic Calibration of Fisheye Camera and LiDAR

TL;DR

This paper introduces a continuous online calibration method for fisheye camera and LiDAR sensors in autonomous vehicles, using mutual information between depth estimates and point clouds, eliminating the need for targets or ground truth.

Contribution

It presents a novel online calibration approach that adapts during vehicle operation using only sensor data, without requiring offline training or calibration targets.

Findings

Achieves high accuracy and precision in calibration.

Operates efficiently in real-time scenarios.

Demonstrates robustness and self-diagnosis capabilities.

Abstract

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.