Core Concepts
Learning high-level semantic-relational concepts enhances SLAM accuracy and scene representation.
Abstract
The content discusses the importance of incorporating high-level semantic-relational concepts like Rooms and Walls into SLAM for improved accuracy. It introduces a novel algorithm based on Graph Neural Networks to infer these concepts from low-level factor graphs. The method is validated in simulated and real datasets, showcasing enhanced performance over baseline approaches. Key highlights include:
- Introduction of semantic-relational entities in SLAM.
- Proposal of a GNN-based algorithm for learning high-level concepts.
- Validation through simulated and real datasets.
- Integration into the S-Graphs+ framework for improved pose and map accuracy.
Stats
"Our approach exhibits a notable reduction of 67% of detection time."
"Ours (Int.) approach for Room and Wall detection demonstrates an improvement of 6.8% with respect to S-Graphs+ [1] baseline."
Quotes
"Our method unfolds in several steps: GNN-based Edge Inference, Clustering, Subgraph Generation."
"In comparison to the current baselines for Room segmentation, our approach exhibits a notable reduction of 67% of detection time."