Learning High-level Semantic-Relational Concepts for SLAM

TL;DR

This paper introduces a Graph Neural Network-based algorithm to learn high-level semantic-relational concepts like Rooms and Walls from low-level factor graphs in SLAM, improving map accuracy and scene understanding.

Contribution

It presents a novel GNN-based method to infer high-level semantic relationships from low-level SLAM graphs, enhancing scene representation and estimation accuracy.

Findings

Improved pose and map accuracy with the new method.

Effective inference of Rooms and Walls from low-level graphs.

Validated on simulated and real datasets.

Abstract

Recent works on SLAM extend their pose graphs with higher-level semantic concepts like Rooms exploiting relationships between them, to provide, not only a richer representation of the situation/environment but also to improve the accuracy of its estimation. Concretely, our previous work, Situational Graphs (S-Graphs+), a pioneer in jointly leveraging semantic relationships in the factor optimization process, relies on semantic entities such as Planes and Rooms, whose relationship is mathematically defined. Nevertheless, there is no unique approach to finding all the hidden patterns in lower-level factor-graphs that correspond to high-level concepts of different natures. It is currently tackled with ad-hoc algorithms, which limits its graph expressiveness. To overcome this limitation, in this work, we propose an algorithm based on Graph Neural Networks for learning high-level semantic-relational concepts that can be inferred from the low-level factor graph. Given a set of mapped Planes our algorithm is capable of inferring Room entities relating to the Planes. Additionally, to demonstrate the versatility of our method, our algorithm can infer an additional semantic-relational concept, i.e. Wall, and its relationship with its Planes. We validate our method in both simulated and real datasets demonstrating improved performance over two baseline approaches. Furthermore, we integrate our method into the S-Graphs+ algorithm providing improved pose and map accuracy compared to the baseline while further enhancing the scene representation.