无需标定物，单图单点实现高鲁棒性激光雷达与相机联合标定
RAVES-Calib: Robust, Accurate and Versatile Extrinsic Self Calibration Using Optimal Geometric Features
- 利用2D-3D点线特征对应建立自动初始估计
- 通过特征分布分析自适应加权，提升标定精度
- 兼容多种传感器，适合真实复杂场景应用
本文提出一种用户友好的激光雷达-相机标定工具包，适用于多种传感器，在无标定物环境下仅需一对激光点和一张图像即可完成标定。方法无需初始变换，对大范围位置与姿态偏差仍具鲁棒性。采用Gluestick管道建立2D-3D点与线特征对应，实现鲁棒且自动的初始猜测。为提升精度，定量分析特征分布对结果的影响，并基于此指标自适应加权各特征代价，从而过滤劣质特征的干扰。在室内外多种传感器组合上进行了大量实验验证，结果表明本方法在鲁棒性与精度上均优于当前最优技术。代码已开源至GitHub，供社区使用。
原文摘要 · Abstract (English)
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.
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