用视觉和GPS构建拓扑地图，实现林地无路径自主导航
Topological mapping for traversability-aware long-range navigation in off-road terrain
- 用全景图像+可通行性边构建地形拓扑图
- 在未训练区域完成两个400平方米林地自主探索
- 适合野外复杂环境长期自主导航研究者
自主机器人在森林等非道路地形中导航为自动化开辟新可能。现有方法多依赖明确路径，本文提出仅使用视觉与GPS的长距离规划、探索与低层控制方法。通过全景快照构成的拓扑地图，节点间连接边包含可通行性信息。提出新型可通行性分析方法，可预测图像中是否存在通往目标的安全路径。节点间导航采用目标条件行为克隆，利用预训练视觉变换器。设计基于前沿的探索规划器，高效覆盖未知且不可通行性未知的非道路区域。该方法成功部署于两个训练时未见的400平方米森林区域，在恶劣导航条件下实现自主探索。
原文摘要 · Abstract (English)
Autonomous robots navigating in off-road terrain like forests open new opportunities for automation. While off-road navigation has been studied, existing work often relies on clearly delineated pathways. We present a method allowing for long-range planning, exploration and low-level control in unknown off-trail forest terrain, using vision and GPS only. We represent outdoor terrain with a topological map, which is a set of panoramic snapshots connected with edges containing traversability information. A novel traversability analysis method is demonstrated, predicting the existence of a safe path towards a target in an image. Navigating between nodes is done using goal-conditioned behavior cloning, leveraging the power of a pretrained vision transformer. An exploration planner is presented, efficiently covering an unknown off-road area with unknown traversability using a frontiers-based approach. The approach is successfully deployed to autonomously explore two 400 meters squared forest sites unseen during training, in difficult conditions for navigation.
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