Name
Title | ||
|---|---|---|
Forming a sparse representation for visual place recognition using a neurorobotic approach. |
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ITSC48978.2021.9564608 | ITSC |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sylvain Colomer | 1 | 0 | 0.34 |
| Nicolas Cuperlier | 2 | 50 | 6.98 |
| Guillaume Bresson | 3 | 20 | 5.83 |
| Olivier Romain | 4 | 141 | 28.20 |