Hierarchical Unsupervised Topological SLAM

TL;DR

This paper introduces a hierarchical unsupervised topological clustering framework that enhances loop detection and closure in SLAM, leading to improved navigation and backend performance across various environments.

Contribution

It presents a novel hierarchical approach for unsupervised topological clustering that improves loop detection and SLAM performance without supervision.

Findings

Significant improvement over state-of-the-art image-based loop detection methods.

Enhanced loop closure and SLAM backend performance.

Effective topological segmentation benefits navigation tasks.

Abstract

In this paper we present a novel framework for unsupervised topological clustering resulting in improved loop. In this paper we present a novel framework for unsupervised topological clustering resulting in improved loop detection and closure for SLAM. A navigating mobile robot clusters its traversal into visually similar topologies where each cluster (topology) contains a set of similar looking images typically observed from spatially adjacent locations. Each such set of spatially adjacent and visually similar grouping of images constitutes a topology obtained without any supervision. We formulate a hierarchical loop discovery strategy that first detects loops at the level of topologies and subsequently at the level of images between the looped topologies. We show over a number of traversals across different Habitat environments that such a hierarchical pipeline significantly improves SOTA image based loop detection and closure methods. Further, as a consequence of improved loop detection, we enhance the loop closure and backend SLAM performance. Such a rendering of a traversal into topological segments is beneficial for downstream tasks such as navigation that can now build a topological graph where spatially adjacent topological clusters are connected by an edge and navigate over such topological graphs.