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Fast and Robust Teach-and-Repeat Navigation Using MixVPR Visual Place Recognition*

2026-10-07 08:06:54
V\'aclav Truhla\v{r}\'ik, Tom\'a\v{s} Pivo\v{n}ka, Libor P\v{r}eu\v{c}il
arXiv_RO
     
Abstract

Teach-and-repeat navigation systems employing advanced visual place recognition techniques for localization exhibit key attributes for long-term mobile robot navigation, such as the ability to operate in unstructured and dynamic environments. However, existing solutions based on deep-learning techniques are computationally demanding, limiting their applicability. This work introduces a novel and efficient teach-and-repeat system built on the modern visual place recognition method MixVPR. Real-world testing demonstrated its ability to operate both indoors and outdoors, achieving robustness and navigation precision comparable to other state-of-the-art systems. In addition, its lower hardware requirements make it suitable for a wide range of robotic platforms and practical applications.

Abstract (translated)

URL

https://arxiv.org/abs/2610.09631

PDF

https://arxiv.org/pdf/2610.09631.pdf

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