High-precision offline mapping and localization system based on ground texture with binary descriptors.

Accurate and robust localization is one of the most basic functional modules of mobile robots. A recent promising approach is to employ a camera that captures ground textures to achieve high-precision localization. However, low robustness and real-time performance are disadvantages of the system due to the time-consuming front-end feature extraction and search methods. To solve these problems, a comprehensive system that has high robustness and real-time performance is proposed, including offline mapping, global localization, local localization and map update. This paper summarizes a method for selecting the optimal front-end for different textures. A fast global localization method based on bag of words is proposed, which can quickly and effectively determine the local positioning area. Experiments are carried out on public datasets and actual indoor scenes to verify the practicability of the system. [ABSTRACT FROM AUTHOR]