S-Nav: Semantic-Geometric Planning for Mobile Robots

TL;DR

S-Nav introduces a semantic-geometric path planning approach for mobile robots that leverages S-Graphs to enhance planning speed, robustness, and path quality in complex indoor environments.

Contribution

It presents S-Nav, a novel hierarchical planner that integrates semantic search with geometric planning using S-Graphs for improved indoor navigation.

Findings

Enhanced planning speed and robustness demonstrated in synthetic environments.

Improved path quality through semantic and geometric integration.

Effective map reconstruction from S-Graphs for better navigation.

Abstract

Path planning is a basic capability of autonomous mobile robots. Former approaches in path planning exploit only the given geometric information from the environment without leveraging the inherent semantics within the environment. The recently presented S-Graphs constructs 3D situational graphs incorporating geometric, semantic, and relational aspects between the elements to improve the overall scene understanding and the localization of the robot. But these works do not exploit the underlying semantic graphs for improving the path planning for mobile robots. To that aim, in this paper, we present S-Nav a novel semantic-geometric path planner for mobile robots. It leverages S-Graphs to enable fast and robust hierarchical high-level planning in complex indoor environments. The hierarchical architecture of S-Nav adds a novel semantic search on top of a traditional geometric planner as well as precise map reconstruction from S-Graphs to improve planning speed, robustness, and path quality. We demonstrate improved results of S-Nav in a synthetic environment.