🤖 AI Summary
This work addresses the challenges of poor sensor compatibility and low robustness to large initialization errors in LiDAR–camera extrinsic calibration under target-free scenarios. We propose a calibration method that requires neither calibration targets nor initial pose estimates. Given only a single RGB image and one LiDAR scan line (i.e., two 3D points), our approach leverages GlueStick to establish automatic 2D–3D point–line feature correspondences. An adaptive weighting scheme—based on geometric distribution—and an optimal feature selection strategy jointly guide a nonlinear optimization to estimate extrinsics. Evaluated across diverse LiDAR–camera configurations (e.g., Velodyne, Ouster, Livox with RGB cameras), our method achieves superior accuracy and robustness over state-of-the-art approaches, converging even with translation and rotation initialization errors up to ±1 m and ±30°. The implementation is open-sourced, significantly enhancing practical deployment efficiency.
📝 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.