Semantic loop closure detection based on graph matching in multi-objects scenes?

Robust loop-closure detection is essential for visual SLAM. Traditional methods often focus on the geometric and visual features in most scenes but ignore the semantic information provided by objects. Based on this consideration, we present a strategy that models the visual scene as semantic sub-graph by only preserving the semantic and geometric information from object detection. To align two sub-graphs efficiently, we use a sparse Kuhn?Munkres algorithm to speed up the search for correspondence among nodes. The shape similarity and the Euclidean distance between objects in the 3-D space are leveraged unitedly to measure the image similarity through graph matching. Furthermore, the proposed approach has been analyzed and compared with the state-of-the-art algorithms at several datasets as well as two indoor real scenes, where the results indicate that our semantic graph-based representation without extracting visual features is feasible for loop-closure detection at potential and competitive precision.

Automated summary

Robust loop-closure detection is important for visual SLAM, and this study proposes a strategy of modeling visual scenes as semantic sub-graphs to efficiently detect loops using semantic and geometric information from object detection. The sparse Kuhn-Munkres algorithm is used to speed up correspondence search, and shape similarity and Euclidean distance are leveraged for graph matching. Analysis and comparison show the feasibility and competitive precision of the proposed semantic graph-based representation for loop-closure detection.