Faster Optimization in S-Graphs Exploiting Hierarchy

TL;DR

This paper introduces an improved hierarchical S-Graph approach for SLAM that reduces computational complexity by marginalizing redundant poses, achieving similar accuracy with significantly faster optimization in large environments.

Contribution

The work presents a hierarchical graph compression method using room-local graphs and marginalization to enhance scalability and efficiency of S-Graphs in large-scale SLAM.

Findings

39.81% reduction in computation time

Maintains similar accuracy to baseline

Effective in large environments

Abstract

3D scene graphs hierarchically represent the environment appropriately organizing different environmental entities in various layers. Our previous work on situational graphs extends the concept of 3D scene graph to SLAM by tightly coupling the robot poses with the scene graph entities, achieving state-of-the-art results. Though, one of the limitations of S-Graphs is scalability in really large environments due to the increased graph size over time, increasing the computational complexity. To overcome this limitation in this work we present an initial research of an improved version of S-Graphs exploiting the hierarchy to reduce the graph size by marginalizing redundant robot poses and their connections to the observations of the same structural entities. Firstly, we propose the generation and optimization of room-local graphs encompassing all graph entities within a room-like structure. These room-local graphs are used to compress the S-Graphs marginalizing the redundant robot keyframes within the given room. We then perform windowed local optimization of the compressed graph at regular time-distance intervals. A global optimization of the compressed graph is performed every time a loop closure is detected. We show similar accuracy compared to the baseline while showing a 39.81% reduction in the computation time with respect to the baseline.