基于深度学习局部特征的视觉地点识别实现多平台示教-重复导航
Multi-Platform Teach-and-Repeat Navigation by Visual Place Recognition Based on Deep-Learned Local Features
- Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague（捷克布拉格技术大学信息学、机器人学与控制论研究所）
- Faculty of Electrical Engineering, Czech Technical University in Prague（捷克布拉格技术大学电气工程学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出基于深度学习局部特征视觉地点识别的多平台示教-重复导航系统，结合新型水平偏移计算与公开数据集，实验证明其跨平台、室内外及昼夜鲁棒性优于现有方法。
AI中文摘要：
一致且多变的环境对于移动机器人导航中稳定的视觉定位与建图仍是一项挑战。适用于此类环境的一种可能方法是基于外观的示教-重复导航，它依赖简化的定位和反应式机器人运动控制，且完全不需要标准建图。本工作为此类系统带来了一种基于视觉地点识别技术的创新解决方案。本文的主要贡献在于采用了一种新的视觉地点识别技术、一种新颖的水平偏移计算方法，以及用于跨各类移动机器人应用的多平台系统设计。其次，介绍了一个用于基于外观的导航方法实验测试的新公开数据集。此外，本工作还提供了真实世界实验测试，并将所提出的导航系统与其他最先进方法进行性能比较。结果证实，新系统在多个测试场景中优于现有方法，能够在室内和室外运行，并对昼夜场景变化表现出鲁棒性。
英文摘要：
Uniform and variable environments still remain a challenge for stable visual localization and mapping in mobile robot navigation. One of the possible approaches suitable for such environments is appearance-based teach-and-repeat navigation, relying on simplified localization and reactive robot motion control - all without a need for standard mapping. This work brings an innovative solution to such a system based on visual place recognition techniques. Here, the major contributions stand in the employment of a new visual place recognition technique, a novel horizontal shift computation approach, and a multi-platform system design for applications across various types of mobile robots. Secondly, a new public dataset for experimental testing of appearance-based navigation methods is introduced. Moreover, the work also provides real-world experimental testing and performance comparison of the introduced navigation system against other state-of-the-art methods. The results confirm that the new system outperforms existing methods in several testing scenarios, is capable of operation indoors and outdoors, and exhibits robustness to day and night scene variations.
