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Forming a sparse representation for visual place recognition using a neurorobotic approach

2021-09-30 08:26:22
Sylvain Colomer, Nicolas Cuperlier, Guillaume Bresson, Olivier Romain
arXiv_CV
       
Abstract

This paper introduces a novel unsupervised neural network model for visual information encoding which aims to address the problem of large-scale visual localization. Inspired by the structure of the visual cortex, the model (namely HSD) alternates layers of topologic sparse coding and pooling to build a more compact code of visual information. Intended for visual place recognition (VPR) systems that use local descriptors, the impact of its integration in a bio-inpired model for self-localization (LPMP) is evaluated. Our experimental results on the KITTI dataset show that HSD improves the runtime speed of LPMP by a factor of at least 2 and its localization accuracy by 10%. A comparison with CoHog, a state-of-the-art VPR approach, showed that our method achieves slightly better results.

Abstract (translated)

URL

https://arxiv.org/abs/2109.14916

PDF

https://arxiv.org/pdf/2109.14916.pdf

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