RAVES-Calib: Robust, Accurate and Versatile Extrinsic Self Calibration Using Optimal Geometric Features

TL;DR

RAVES-Calib is a versatile, robust LiDAR-camera calibration toolkit that uses minimal data, adaptively weights features for accuracy, and outperforms state-of-the-art methods in diverse environments.

Contribution

The paper introduces a novel calibration approach that requires only a single laser point pair and image, with adaptive feature weighting for improved accuracy and robustness.

Findings

Achieves superior accuracy over SOTA techniques.

Remains robust with large initial pose errors.

Validated across various sensors and environments.

Abstract

In this paper, we present a user-friendly LiDAR-camera calibration toolkit that is compatible with various LiDAR and camera sensors and requires only a single pair of laser points and a camera image in targetless environments. Our approach eliminates the need for an initial transform and remains robust even with large positional and rotational LiDAR-camera extrinsic parameters. We employ the Gluestick pipeline to establish 2D-3D point and line feature correspondences for a robust and automatic initial guess. To enhance accuracy, we quantitatively analyze the impact of feature distribution on calibration results and adaptively weight the cost of each feature based on these metrics. As a result, extrinsic parameters are optimized by filtering out the adverse effects of inferior features. We validated our method through extensive experiments across various LiDAR-camera sensors in both indoor and outdoor settings. The results demonstrate that our method provides superior robustness and accuracy compared to SOTA techniques. Our code is open-sourced on GitHub to benefit the community.