Loop Closure Detection Based on Object-level Spatial Layout and Semantic Consistency

TL;DR

This paper introduces a novel object-based loop closure detection method for SLAM that leverages 3D scene graph semantics and spatial layout to improve robustness under large viewpoint changes.

Contribution

It presents a new object-level data association and graph matching approach that enhances loop closure detection by utilizing semantic and spatial information in 3D scene graphs.

Findings

More accurate 3D semantic maps achieved

Enhanced robustness in large viewpoint changes

Outperforms point-based and object-based methods

Abstract

Visual simultaneous localization and mapping (SLAM) systems face challenges in detecting loop closure under the circumstance of large viewpoint changes. In this paper, we present an object-based loop closure detection method based on the spatial layout and semanic consistency of the 3D scene graph. Firstly, we propose an object-level data association approach based on the semantic information from semantic labels, intersection over union (IoU), object color, and object embedding. Subsequently, multi-view bundle adjustment with the associated objects is utilized to jointly optimize the poses of objects and cameras. We represent the refined objects as a 3D spatial graph with semantics and topology. Then, we propose a graph matching approach to select correspondence objects based on the structure layout and semantic property similarity of vertices' neighbors. Finally, we jointly optimize camera trajectories and object poses in an object-level pose graph optimization, which results in a globally consistent map. Experimental results demonstrate that our proposed data association approach can construct more accurate 3D semantic maps, and our loop closure method is more robust than point-based and object-based methods in circumstances with large viewpoint changes.