Non-Uniform Network Mapping and Localization

The purpose of this paper is to establish a new simultaneous localization and mapping (SLAM) framework, which can make better use of the spatial structure of the environment, to improve the robustness and reliability of the system. To locate, a good map should be not only a representation of the real environment, but also a highly compressed and intelligently organized geography information of the environment. Furthermore, the map structure should be stable and robust to inevitable errors. Inspired by human positioning ability, we developed a non-uniform network mapping and localization algorithm (NU-SLAM) based on the complex network theory. Landmarks are classified and sparsely connected according to their localization ability. Therefore, data association and information transmission can be carried out hierarchically with low computational cost and robustness to data association errors. In this paper, a new visual to the mapping and localization problem is proposed. Simulation results show that the algorithm can achieve good results.