Relative-Absolute Map Filter for Simultaneous Localization and Mapping

Abstract

In this paper, a new algorithm, relative-absolute map filter (RAMF), is proposed to solve the simultaneous localization and mapping problem. Compared with FastSLAM, which adopts many absolute maps to describe the relationship between features, RAMF utilizes only one relative map instead. By fusing the information of relative map and absolute map, RAMF can create a more accurate map. Moreover, the embedded particle filter in RAMF can handle robot localization. Simulation results show that RAMF has better performance than FastSLAM and UKF SLAM in the noisy robot motion