Joint Intrinsic and Extrinsic Calibration of Perception Systems Utilizing a Calibration Environment

Abstract

Basically all multi-sensor systems must calibrate their sensors to exploit their full potential for state estimation such as mapping and localization. In this letter, we investigate the problem of extrinsic and intrinsic calibration of perception systems. Traditionally, targets in the form of checkerboards or uniquely identifiable tags are used to calibrate those systems. We propose to use a whole calibration environment as a target that supports the intrinsic and extrinsic calibration of different types of sensors. By doing so, we are able to calibrate multiple perception systems with different configurations, sensor types, and sensor modalities. Our approach does not rely on overlaps between sensors which is often otherwise required when using classical targets. The main idea is to relate the measurements for each sensor to a precise model of the calibration environment. For this, we can choose for each sensor a specific method that best suits its calibration. Then, we estimate all intrinsics and extrinsics jointly using least squares adjustment. For the final evaluation of a LiDAR-to-camera calibration of our system, we propose an evaluation method that is independent of the calibration. This allows for quantitative evaluation between different calibration methods. The experiments show that our proposed method is able to provide reliable calibration.