[
    {
        "id": "osp-18225",
        "type": "article-journal",
        "title": "Fast and Robust Teach-and-Repeat Navigation Using MixVPR Visual Place Recognition*",
        "author": [
            {
                "family": "Truhlařík",
                "given": "Václav"
            },
            {
                "family": "Pivoňka",
                "given": "Tomáš"
            },
            {
                "family": "Přeučil",
                "given": "Libor"
            }
        ],
        "URL": "https://omanscience.com/en/articles/fast-and-robust-teach-and-repeat-navigation-using-mixvpr-visual-place-recognition",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "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."
    }
]