Galibr: Targetless LiDAR-Camera Extrinsic Calibration Method via Ground Plane Initialization

TL;DR

Galibr offers an automatic, targetless LiDAR-camera calibration method that leverages ground plane and edge features, providing a robust solution suitable for natural environments and various vehicle platforms.

Contribution

Introduces Galibr, a novel fully automatic calibration approach using ground plane initialization and edge matching, eliminating the need for targets in natural settings.

Findings

Achieves accurate calibration in unstructured environments

Demonstrates robustness on KITTI and KAIST datasets

Improves calibration performance over existing methods

Abstract

With the rapid development of autonomous driving and SLAM technology, the performance of autonomous systems using multimodal sensors highly relies on accurate extrinsic calibration. Addressing the need for a convenient, maintenance-friendly calibration process in any natural environment, this paper introduces Galibr, a fully automatic targetless LiDAR-camera extrinsic calibration tool designed for ground vehicle platforms in any natural setting. The method utilizes the ground planes and edge information from both LiDAR and camera inputs, streamlining the calibration process. It encompasses two main steps: an initial pose estimation algorithm based on ground planes (GP-init), and a refinement phase through edge extraction and matching. Our approach significantly enhances calibration performance, primarily attributed to our novel initial pose estimation method, as demonstrated in unstructured natural environments, including on the KITTI dataset and the KAIST quadruped dataset.