A new method for indoor low-cost mobile robot SLAM

Abstract

Simultaneous Localizatoin and Mapping (SLAM) is an active area of robot research and the location technology of mobile robots is a very critical issue in the filed of SLAM. Low cost and high performance can not be balanced in commercial robots and we plan to achieve better performance with low-cost sensors. This paper focuses on the study of complex indoor environment and a new method of point cloud matching and low-cost mobile robot localization is presented. Firstly, we change the data within a range of distance from laser to image by building grid maps and we consider each map has no scaling relation to others. Second, fast Fourier transformation is used to get the rotation angle. In order to get translation parameters, one-dimensional Fourier transformation is used to horizontal projection and vertical projection of map. We can build maps based on positioning results. Finally, we perform comparative experiments with other common methods.