用雷达直接优化位姿图，雨雪天也能精准定位。
Dr-PoGO: Direct Radar Pose-Graph Optimization
- 直接使用雷达数据做位姿估计与回环检测
- 在300公里真实道路数据上达到顶尖性能
- 适合自动驾驶等恶劣天气场景应用
本文提出Dr-PoGO，一种基于2D旋转毫米波雷达的同步定位与建图方法。与依赖视线的相机或激光雷达不同，毫米波雷达可在尘土、雨雪等恶劣环境下保持感知能力。现有雷达SLAM多依赖点云或特征提取进行运动估计，而Dr-PoGO采用直接位姿注册（DRO）技术实现里程计与回环闭合。通过现成的雷达场景识别算法RaPlace获取回环候选，再结合视觉特征进行粗到精的初始变换估计，最终在位姿图优化中全局优化轨迹。在多种真实汽车环境下的300公里数据上验证了其领先性能。代码已开源：https://github.com/utiasASRL/dr_pogo。
原文摘要 · Abstract (English)
This paper introduces Dr-PoGO, a method for Simultaneous Localization And Mapping (SLAM) using a 2D spinning radar. Unlike cameras or lidars that require line-of-sight, millimetre-wave radars can `see' through dust, falling snow, rain, etc. Accordingly, it is a great modality for robust perception regardless of the weather conditions. While most existing radar-based SLAM methods rely on the extraction of point clouds or features to perform ego-motion estimation, Dr-PoGO leverages direct registration techniques for odometry (DRO) and loop-closure registration. An off-the-shelf radar-focused place recognition algorithm, RaPlace, provides loop-closure candidates. As RaPlace does not provide relative transformations, Dr-PoGO introduces a coarse-to-fine registration that uses visual features and descriptors to obtain an initial guess for the direct transformation refinement. The global trajectory is optimized in a pose-graph optimization. Dr-PoGO demonstrates state-of-the-art performance over 300km of data in various real-world automotive environments. Our implementation is publicly available: https://github.com/utiasASRL/dr_pogo.
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