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

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Sylvain Colomer, Nicolas Cuperlier, Guillaume Bresson, Olivier Romain

Computer Vision and Pattern Recognition · Submitted 30 Sep 2021

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim 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%.

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arXiv ID2109.14916 (version 1)
Pith IDSLYFU4V2, Pith's permanent record of this paper and its review
CitationGuillaume Bresson, Nicolas Cuperlier, Olivier Romain, Sylvain Colomer. "Forming a sparse representation for visual place recognition using a neurorobotic approach." arXiv:2109.14916 (2021). https://pith.science/paper/SLYFU4V2
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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,318 input tokens.

What the abstract itself states, as probabilities: claims first 0.13 · states limitation 0.08 · human participants 0.02 · controlled comparison 0.77 · randomized assignment 0.01 · states quantitative result 0.99 · compares with prior results 0.98 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

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.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 This paper introduces a novel unsupervised neural network model for visual information encoding which aims to address the problem of large-scale visual localization. contribution 1.00 0.77 0.05 asserted 0.99
s1 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. method 0.99 0.55 0.03 asserted 0.99
s2 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. contribution 0.52 0.50 0.04 asserted 0.79
s3 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%. result 1.00 0.97 0.62 asserted 1.00
s4 A comparison with CoHog, a state-of-the-art VPR approach, showed that our method achieves slightly better results. result 1.00 0.93 0.15 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.86.

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not examined
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  • references and citing sentences
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  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original 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.

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Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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