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arXiv · 2310.04802

Hierarchical Unsupervised Topological SLAM

Ayush Sharma·Yash Mehan·Pradyumna Dasu·Sourav Garg·Madhava Krishna
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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.

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Ayush Sharma, Yash Mehan, Pradyumna Dasu, Sourav Garg, Madhava Krishna. 2023-10-07. Hierarchical Unsupervised Topological SLAM. https://arxiv.org/abs/2310.04802

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