无需标记物和运动信息，自动完成多激光雷达精准校准
Multi-LiCa: A Motion and Targetless Multi LiDAR-to-LiDAR Calibration Framework
- 分两步：先特征匹配粗对齐，再用GICP+成本匹配精调
- 在不同传感器布局下校准精度优于或媲美现有方法
- 支持任意数量激光雷达，适用于无重叠或部分重叠场景
当前自动驾驶车辆依赖多种传感器感知环境，其相对位置关系必须精确已知。本文提出Multi-LiCa，一种无需额外传感器模态或初始变换输入的全自动、无运动、无目标多激光雷达外参标定方法。该方法采用两阶段流程：首先通过特征匹配实现粗对齐，再结合GICP算法与基于成本的匹配策略进行精细注册。本框架可适用于任意数量和布局的传感器，只要各传感器视场存在部分重叠即可。实验表明，该方法在不同传感器配置和场景下具有更强泛化能力，校准精度达到或超过现有方法。框架已集成于ROS 2，亦可独立使用，源代码公开于https://github.com/TUMFTM/Multi_LiCa。
原文摘要 · Abstract (English)
Today's autonomous vehicles rely on a multitude of sensors to perceive their environment. To improve the perception or create redundancy, the sensor's alignment relative to each other must be known. With Multi-LiCa, we present a novel approach for the alignment, e.g. calibration. We present an automatic motion- and targetless approach for the extrinsic multi LiDAR-to-LiDAR calibration without the need for additional sensor modalities or an initial transformation input. We propose a two-step process with feature-based matching for the coarse alignment and a GICP-based fine registration in combination with a cost-based matching strategy. Our approach can be applied to any number of sensors and positions if there is a partial overlap between the field of view of single sensors. We show that our pipeline is better generalized to different sensor setups and scenarios and is on par or better in calibration accuracy than existing approaches. The presented framework is integrated in ROS 2 but can also be used as a standalone application. To build upon our work, our source code is available at: https://github.com/TUMFTM/Multi_LiCa.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。