This paper introduces a new method for representing two-dimensional maps, and shows how this representation may be applied to concurrent localization and mapping problems involving multiple robots. We introduce the notion of a manifold map; this representation takes maps out of the plane and onto a two-dimensional surface embedded in a higher-dimensional space. Compared with standard planar maps, the key advantage of the manifold representation is self-consistency: as we will show, manifold maps do not suffer from the `cross over' problem that planar maps commonly exhibit in environments containing loops. This self-consistency facilitates a number of important autonomous capabilities, including robust retro-traverse, lazy loop closure, active loop closure using robot rendezvous, and, ultimately, autonomous exploration.

We describe the design and experimental validation of a large heterogeneous mobile robot team built for the DARPA Software for Distributed Robotics (SDR) program. The core challenge for the SDR program was to develop a multi-robot system capable of carrying out a specific mission: to deploy a large number of robots into an unexplored building, map the building interior, detect and track intruders, and transmit all of the above information to a remote operator. To satisfy these requirements, we developed a heterogeneous robot team consisting of approximately 80 robots. We sketch the key technical elements of this team, focusing on the novel aspects, and present selected results from supervised experiments conducted in a 600 m 2 indoor environment. 1

This paper studies the use of wireless Ethernet (Wi-Fi) as a localization sensor for mobile robots. Wi-Fi-based localization relies on the existence of one or more Wi-Fi devices in the environment to act as beacons, and uses signal strength information from those beacons to localize the robot. Through the experiments described in this paper, we explore the general properties of Wi-Fi in indoor environments, and assess both the accuracy and utility of Wi-Fibased localization.

We propose an on-line algorithm for simultaneous localization and mapping of dynamic environments. Our algorithm is capable of differentiating static and dynamic parts of the environment and representing them appropriately on the map. Our approach is based on maintaining two occupancy grids. One grid models the static parts of the environment, and the other models the dynamic parts of the environment. The union of the two grid maps provides a complete description of the environment over time. We also maintain a third map containing information about static landmarks detected in the environment. These landmarks provide the robot with localization. Results in simulation and real robots experiments show the efficiency of our approach and also show how the differentiation of dynamic and static entities in the environment and SLAM can be mutually beneficial.