Learning High-level Semantic-Relational Concepts for SLAM
arxiv(2023)
摘要
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.
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