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

作者：Václav Truhlarík, Tomáš Pivoňka, L. Přeučil · 发表于：European Conference on Mobile Robots · 年份：2025 · DOI：10.1109/ECMR65884.2025.11163381 · 被引用次数：1 · 研究领域：Computer Science

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.