基于MixVPR视觉位置识别的快速鲁棒教-复导航
Fast and Robust Teach-and-Repeat Navigation Using MixVPR Visual Place Recognition*
- Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague（捷克理工大学信息学、机器人与控制论研究所）
- Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague（捷克理工大学电气工程学院控制论系）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出基于MixVPR的教-复导航系统，在室内外实现鲁棒精确导航，且计算需求低，适用于多种机器人平台。
AI中文摘要：
采用先进视觉位置识别技术进行定位的教-复导航系统，具备长期移动机器人导航的关键属性，例如能够在非结构化和动态环境中运行。然而，现有基于深度学习技术的解决方案计算需求高，限制了其适用性。本工作引入了一种新颖且高效的教-复系统，构建于现代视觉位置识别方法MixVPR之上。真实世界测试表明，该系统能够在室内和室外运行，达到了与其他先进系统相当的鲁棒性和导航精度。此外，其较低的硬件需求使其适用于广泛的机器人平台和实际应用。
英文摘要：
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.
