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

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.

Robotics (cs.RO)

arXiv:2610.09631 [cs.RO]

(or arXiv:2610.09631v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.09631 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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