Advanced Situational Graphs for Robot Navigation in Structured Indoor Environments

TL;DR

This paper introduces S-Graphs+, an advanced five-layered situational graph model that enhances robot navigation by integrating geometric, semantic, and topological information in structured indoor environments.

Contribution

The paper presents S-Graphs+, a novel five-layered graph structure that improves environmental understanding and robot pose estimation in indoor navigation tasks.

Findings

S-Graphs+ outperforms previous models in environment extraction accuracy.

S-Graphs+ enhances robot pose estimation accuracy.

The five-layered model extends situational awareness in indoor navigation.

Abstract

Mobile robots extract information from its environment to understand their current situation to enable intelligent decision making and autonomous task execution. In our previous work, we introduced the concept of Situation Graphs (S-Graphs) which combines in a single optimizable graph, the robot keyframes and the representation of the environment with geometric, semantic and topological abstractions. Although S-Graphs were built and optimized in real-time and demonstrated state-of-the-art results, they are limited to specific structured environments with specific hand-tuned dimensions of rooms and corridors. In this work, we present an advanced version of the Situational Graphs (S-Graphs+), consisting of the five layered optimizable graph that includes (1) metric layer along with the graph of free-space clusters (2) keyframe layer where the robot poses are registered (3) metric-semantic layer consisting of the extracted planar walls (4) novel rooms layer constraining the extracted planar walls (5) novel floors layer encompassing the rooms within a given floor level. S-Graphs+ demonstrates improved performance over S-Graphs efficiently extracting the room information while simultaneously improving the pose estimate of the robot, thus extending the robots situational awareness in the form of a five layered environmental model.