Development of Collaborative SLAM Algorithm for Team of Robots

Simultaneous Localization and Mapping (SLAM) is a fundamental problem for building truly automatic robots. Varieties of methods and algorithms have been generated, and applied into mobile robots during the last thirty years. However, each algorithm has its strength and weakness. This thesis studies the most recent published techniques in the field of mobile robot SLAM. Specifically, it focuses on investigating robot path and landmark position estimating errors made by different methods. The Hybrid method, which uses FastSLAM method as front-end and uses EKF-SLAM method as back-end, combines both methods advantages, producing smaller errors on estimating robot pose. The Hybrid method solves the single robot SLAM problems by summing the weighted mean values of each particle in FastSLAM. The contributions of this thesis is it presents an alternate mapping algorithm that extends this single-robot Hybrid SLAM algorithm to a multi-robot SLAM algorithm. In this algorithm, each robot draws map of the environment separately, and robots could transfer their mapping information into a central computer. The central computer could merge the landmark positions from different robots. At last, a revised landmark position as well as its covariance will be calculated. Landmark positions are fused together according to two robots feature information by using Kalman Filters.