SuReNav：基于超像素图的约束放松用于过约束环境中的导航
SuReNav: Superpixel Graph-based Constraint Relaxation for Navigation in Over-constrained Environments
AI总结：
本文提出SuReNav方法，通过超像素图构建区域约束，利用图神经网络实现安全高效导航，适用于半静态环境中过约束规划问题，提升导航的人类类比性能。
摘要：
我们针对半静态环境中过约束规划问题，提出SuReNav方法，通过超像素图构建区域约束，利用图神经网络训练于人类示范数据，实现安全高效的导航。框架包含三个组件：1）带有区域约束的超像素图地图生成，2）利用图神经网络进行区域约束放松，3）放松、规划和执行的交织过程。在2D语义地图和3D OpenStreetMap地图上评估，实现最高的人类类比得分，同时保持效率与安全的平衡。最后在现实城市导航中展示其可扩展性和泛化能力。代码和视频可在https://sure-nav.github.io/获取。
英文摘要：
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/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
