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.

翻译：均匀与多变环境仍然是移动机器人导航中实现稳定视觉定位与建图的挑战。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，该方法依赖简化的定位与反应式机器人运动控制——整个过程无需标准建图。本研究为该类系统提出了一种基于视觉地点识别技术的创新解决方案。本工作的主要贡献在于：采用了一种新的视觉地点识别技术、提出了一种新颖的水平偏移计算方法，以及设计了适用于各类移动机器人的多平台系统架构。其次，本研究还引入了一个用于基于外观导航方法实验测试的新公开数据集。此外，工作还通过实际环境实验测试，将所提出的导航系统与其他先进方法进行了性能对比。实验结果表明，新系统在多种测试场景中优于现有方法，具备室内外运行能力，并对昼夜场景变化表现出良好的鲁棒性。