Distributed autonomous mapping of indoor environments

Abstract

This paper describes the results of a Joint Experiment performed on behalf of the MAST CTA. The system developed for the Joint Experiment makes use of three robots which work together to explore and map an unknown environment. Each of the robots used in this experiment is equipped with a laser scanner for measuring walls and a camera for locating doorways. Information from both of these types of structures is concurrently incorporated into each robot's local map using a graph based SLAM technique. A Distributed-Data-Fusion algorithm is used to efficiently combine local maps from each robot into a shared global map. Each robot computes a compressed local feature map and transmits it to neighboring robots, which allows each robot to merge its map with the maps of its neighbors. Each robot caches the compressed maps from its neighbors, allowing it to maintain a coherent map with a common frame of reference. The robots utilize an exploration strategy to efficiently cover the unknown environment which allows collaboration on an unreliable communications channel. As each new branching point is discovered by a robot, it broadcasts the information about where this point is along with the robot's path from a known landmark to the other robots. When the next robot reaches a dead-end, new branching points are allocated by auction. In the event of communication interruption, the robot which observed the branching point will eventually explore it; therefore, the exploration is complete in the face of communication failure.