单次采集自动完成相机与激光雷达标定，无需目标物
MFCalib: Single-shot and Automatic Extrinsic Calibration for LiDAR and Camera in Targetless Environments Based on Multi-Feature Edge
- 融合深度连续、不连续及强度不连续边缘信息进行标定
- 单次采集即达现有顶尖方法水平，复杂场景仍保持高精度
- 适合自动驾驶、机器人等需快速部署的实时系统
本文提出MFCalib，一种在无标定目标环境下，仅需单次数据采集即可自动完成激光雷达与相机外参标定的新方法。核心思路是综合利用深度连续与不连续边缘，以及平面上的强度不连续边缘，显著提升标定精度与鲁棒性。针对深度不连续边缘存在的不确定性问题，基于激光雷达物理测量原理构建光束模型，有效缓解了由激光束宽度引起的边缘膨胀问题。大量实验表明，MFCalib在多种场景下均优于当前最先进的无目标标定方法，且在单次采集条件下实现甚至超越多场景标定的精度。为促进社区发展，代码已开源至GitHub。
原文摘要 · Abstract (English)
This paper presents MFCalib, an innovative extrinsic calibration technique for LiDAR and RGB camera that operates automatically in targetless environments with a single data capture. At the heart of this method is using a rich set of edge information, significantly enhancing calibration accuracy and robustness. Specifically, we extract both depth-continuous and depth-discontinuous edges, along with intensity-discontinuous edges on planes. This comprehensive edge extraction strategy ensures our ability to achieve accurate calibration with just one round of data collection, even in complex and varied settings. Addressing the uncertainty of depth-discontinuous edges, we delve into the physical measurement principles of LiDAR and develop a beam model, effectively mitigating the issue of edge inflation caused by the LiDAR beam. Extensive experiment results demonstrate that MFCalib outperforms the state-of-the-art targetless calibration methods across various scenes, achieving and often surpassing the precision of multi-scene calibrations in a single-shot collection. To support community development, we make our code available open-source on GitHub.
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