A simultaneous localization and mapping algorithm for sensors with low sampling rate and its application to autonomous mobile robots

Abstract

In this paper we suggest a Simultaneous Localization and Mapping (SLAM) algorithm for Autonomous Mobile Robots (AMRs) which have LiDAR (light detection and ranging) type planar sensors with low sampling rate, e.g., less than 1 Hz. The proposed method uses 2-dimensional point clouds for its internal occupancy map representation and applies Point Set Registration (PSR) algorithms for mapping and localization. The approach is validated on both synthetic and real-world data. The results demonstrate that the proposed method is efficient, even when the observations are imprecise as well as the difference between consecutive measurements is high in terms of position and orientation.