Line-based Automatic Extrinsic Calibration of LiDAR and Camera

Abstract

Reliable real-time extrinsic parameters of 3D Light Detection and Ranging (LiDAR) and camera are a key component of multi-modal perception systems. However, extrinsic transformation may drift gradually during operation, which can result in decreased accuracy of perception system. To solve this problem, we propose a line-based method that enables automatic online extrinsic calibration of LiDAR and camera in real-world scenes. Herein, the line feature is selected to constrain the extrinsic parameters for its ubiquity. Initially, the line features are extracted and filtered from point clouds and images. Afterwards, an adaptive optimization is utilized to provide accurate extrinsic parameters. We demonstrate that line features are robust geometric features that can be extracted from point clouds and images, thus contributing to the extrinsic calibration. To demonstrate the benefits of this method, we evaluate it on KITTI benchmark with ground truth value. The experiments verify the accuracy of the calibration approach. In online experiments on hundreds of frames, our approach automatically corrects miscalibration errors and achieves an accuracy of 0.2 degrees, which verifies its applicability in various scenarios. This work can provide basis for perception systems and further improve the performance of other algorithms that utilize these sensors.