Robust Extrinsic Self-Calibration of Camera and Solid State LiDAR

TL;DR

This paper introduces a robust extrinsic calibration method for monocular cameras and solid-state LiDARs that leverages depth discontinuities and geometric constraints to improve accuracy and outperforms existing algorithms in real-world tests.

Contribution

It presents a novel calibration approach that utilizes depth discontinuous measurements and geometric rules, enhancing robustness and accuracy over prior methods.

Findings

The proposed method achieves superior calibration accuracy in real experiments.

Utilizes depth discontinuities to retain more valid features for calibration.

Outperforms existing algorithms in qualitative and quantitative evaluations.

Abstract

This letter proposes an extrinsic calibration approach for a pair of monocular camera and prism-spinning solid-state LiDAR. The unique characteristics of the point cloud measured resulting from the flower-like scanning pattern is first disclosed as the vacant points, a type of outlier between foreground target and background objects. Unlike existing method using only depth continuous measurements, we use depth discontinuous measurements to retain more valid features and efficiently remove vacant points. The larger number of detected 3D corners thus contain more robust a priori information than usual which, together with the 2D corners detected by overlapping cameras and constrained by the proposed circularity and rectangularity rules, produce accurate extrinsic estimates. The algorithm is evaluated with real field experiments adopting both qualitative and quantitative performance criteria, and found to be superior to existing algorithms. The code is available on GitHub.