Multi-robot simultaneous localization and mapping using D-SLAM framework

Abstract

This paper presents an algorithm for the multi-robot simultaneous localization and mapping (SLAM) problem with the robot initial locations completely unknown. Each robot builds its own local map using the traditional extended Kalman filter (EKF) SLAM algorithm. We provide a new method to fuse the local maps into a jointly maintained global map by first transforming the local map state estimate into relative location information and then conducting the fusion using the decoupled SLAM (D-SLAM) framework (Wang et al., 2007). An efficient algorithm to find the map overlap and corresponding beacons across the maps is developed from a point feature based medical image registration method and the joint compatibility test. By adding the robot initial pose of each local map into the global map state, the algorithm shows valuable properties. Simulation results are provided to illustrate the effectiveness of the algorithm.