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arXiv · 1902.09516

Condition-Invariant Multi-View Place Recognition

Jose M. Facil·Daniel Olid·Luis Montesano·Javier Civera
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Abstract

Visual place recognition is particularly challenging when places suffer changes in its appearance. Such changes are indeed common, e.g., due to weather, night/day or seasons. In this paper we leverage on recent research using deep networks, and explore how they can be improved by exploiting the temporal sequence information. Specifically, we propose 3 different alternatives (Descriptor Grouping, Fusion and Recurrent Descriptors) for deep networks to use several frames of a sequence. We show that our approaches produce more compact and best performing descriptors than single- and multi-view baselines in the literature in two public databases.

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Jose M. Facil, Daniel Olid, Luis Montesano, Javier Civera. 2019-02-25. Condition-Invariant Multi-View Place Recognition. https://arxiv.org/abs/1902.09516

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