Related papers: Advanced Situational Graphs for Robot Navigation in Structured Indoor Environments

Advanced Situational Graphs for Robot Navigation in Structured Indoor Environments

URL: http://arxiv.org/abs/2211.08754v1
Date: Wed, 16 Nov 2022 08:30:05 GMT
Title: Advanced Situational Graphs for Robot Navigation in Structured Indoor Environments
Authors: Hriday Bavle, Jose Luis Sanchez-Lopez, Muhammad Shaheer, Javier Civera, Holger Voos
Abstract summary: We present an advanced version of the Situational Graphs (S-Graphs+), consisting of the five layered optimizable graph. S-Graphs+ demonstrates improved performance over S-Graphs efficiently extracting the room information.
Score: 9.13466172688693
License: http://creativecommons.org/licenses/by/4.0/
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.

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