SuReNav: Superpixel Graph-based Constraint Relaxation for Navigation in Over-constrained Environments

TL;DR

SuReNav introduces a superpixel graph-based approach with neural network constraint relaxation for safe, efficient navigation in over-constrained environments, mimicking human navigation behavior.

Contribution

It presents a novel framework combining superpixel graphs and neural network-based constraint relaxation for improved navigation in complex environments.

Findings

Achieved the highest human-likeness score in navigation tasks.

Balanced efficiency and safety effectively in diverse maps.

Demonstrated scalability on real-world robot navigation.

Abstract

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/.