SwarMap: Occupancy Grid Mapping with a Robotic Swarm

Abstract

In recent years, robotic swarms have been studied as an alternative to replace complex and expensive robots in the execution of different types of tasks. In this paper, we propose an approach to cooperatively build occupancy grid maps using large groups of simpler robots. Robots randomly explore the environment and update a shared occupancy grid stored in the cloud using Bayesian Filters. They localize themselves based on odometry and use pair-to-pair neighborhood communication to exchange pose information and reduce the uncertainty through a Kalman Filter. Simulated and real experiments are performed to show the effectiveness and scalability of the proposed approach.