Simultaneous Localization and Mapping of Mobile Robot with Research and Implementation

Abstract

In the process of autonomous navigation, mobile robots need to build maps of the surrounding environment and simultaneous localization. The Rao-Blackwellzed particle filter algorithm is one of the methods to efficiently solve the problem that simultaneous localization and mapping of mobile robots. At present, Mapping of inconsistent have long been the focus of research. In order to solve this problem, this paper provides an algorithm which uses high-precision Laser data to correct the proposed distribution based on odometer readings, focus sampling on the possible areas of observation information, reduces the error of proposed distribution, and establish a more accurate map environment. Finally, the experimental verification was carried out on the Bulldog mobile robot platform equipped with a 16-line Laser sensor. The results show that the optimized method of performance is more stable, can improves the diversity of particles and creates highprecision environmental maps online in real time.