用超像素图缓解复杂环境导航约束，让机器人像人一样安全高效避障。
SuReNav: Superpixel Graph-based Constraint Relaxation for Navigation in Over-constrained Environments
- 构建超像素图地图并引入区域约束，模拟人类导航思维。
- 基于人类示范训练图神经网络，实现安全与效率的动态平衡。
- 适用于真实城市环境，支持四足机器人规模化部署。
本文针对半静态环境中过度约束的路径规划问题，提出一种模仿人类安全高效导航的超像素图约束松弛方法（SuReNav）。该框架包含三个部分：1）基于区域约束生成超像素图地图；2）利用人类示范训练图神经网络，实现区域约束的智能松弛以保障导航安全与效率；3）通过松弛、规划与执行的交替进行，完成完整导航。在2D语义地图和来自OpenStreetMap的3D地图上评估，本方法在完整导航的人类相似度评分中表现最优，同时保持效率与安全性的良好权衡。最终在真实城市环境中，使用四足机器人Spot验证了其可扩展性与泛化能力。代码与视频见https://sure-nav.github.io/。
原文摘要 · Abstract (English)
We address the over-constrained planning problem in semi-static environments. The planning objective is to find a best-effort solution that avoids all hard constraint regions while minimally traversing the least risky areas. Conventional methods often rely on pre-defined area costs, limiting generalizations. Further, the spatial continuity of navigation spaces makes it difficult to identify regions that are passable without overestimation. To overcome these challenges, we propose SuReNav, a superpixel graph-based constraint relaxation and navigation method that imitates human-like safe and efficient navigation. Our framework consists of three components: 1) superpixel graph map generation with regional constraints, 2) regional-constraint relaxation using graph neural network trained on human demonstrations for safe and efficient navigation, and 3) interleaving relaxation, planning, and execution for complete navigation. We evaluate our method against state-of-the-art baselines on 2D semantic maps and 3D maps from OpenStreetMap, achieving the highest human-likeness score of complete navigation while maintaining a balanced trade-off between efficiency and safety. We finally demonstrate its scalability and generalization performance in real-world urban navigation with a quadruped robot, Spot. Code and Videos are available at https://sure-nav.github.io/.
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