用卫星图训练模型，让机器人从图像中看懂远距离可通行区域。
Distilling Global Traversability Priors for Image-based Affordance Prediction in Off-road Environments
- 用卫星图生成路径数据，监督网络学习远距离可通行性。
- 在离线测试中导航性能提升超10%，真实场景干预次数减少。
- 适合做野外长距离自主导航的团队参考，减少人工标注依赖。
非结构化地形中的自主导航常因视野受限产生短视行为。传统基于激光雷达或相机构建的度量地图受深度感知范围限制，超出映射范围的数据被丢弃，导致决策次优。为恢复远距离信息，本文直接从第一人称视角（FPV）图像中提取远距离可通行性前沿。利用卫星影像计算图像/位姿对的可行导航路径，作为监督信号训练网络，显著降低对大量人工示范数据的需求。实验表明，该方法在多个离线基准上实现超过10%的性能提升，并在一系列真实世界实验中减少了人类干预次数。更多信息见 https://theairlab.org/ss_frontiers_iros。
原文摘要 · Abstract (English)
Standard methods for autonomous navigation in unstructured terrain are prone to myopic behaviors in long-horizon scenarios. The use of metric maps built from LiDAR or cameras provides necessary local geometry and semantic information but is strictly limited by depth sensing range. By discarding data beyond the mapping horizon robots suffer from suboptimal, short-sighted decisions. To recover this lost information, we focus on extracting long-range traversability-aware frontiers directly from first-person-view (FPV) images. By leveraging satellite imagery, we compute the set of feasible navigation paths for a dataset of image/pose pairs and use them to supervise our network, reducing the need for extensive human demonstration data. We demonstrate that this approach improves performance in long-range off-road navigation over existing methods by more than 10% in various offline benchmarks and reduces the number of human interventions incurred in a set of real-world experiments. More details can be found at https://theairlab.org/ss_frontiers_iros .
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