Learning metric-topological maps for indoor mobile robot navigation
Artificial Intelligence
Globally Consistent Range Scan Alignment for Environment Mapping
Autonomous Robots
Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans
Journal of Intelligent and Robotic Systems
FastSLAM: a factored solution to the simultaneous localization and mapping problem
Eighteenth national conference on Artificial intelligence
Exploring artificial intelligence in the new millennium
A frontier-based approach for autonomous exploration
CIRA '97 Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation
Bridging the Gap Between Simulation and Reality in Urban Search and Rescue
RoboCup 2006: Robot Soccer World Cup X
IEEE Transactions on Robotics
Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters
IEEE Transactions on Robotics
RoboCup 2007: Robot Soccer World Cup XI
Distributed consensus algorithms for merging feature-based maps with limited communication
Robotics and Autonomous Systems
Coordinated action in a heterogeneous rescue team
RoboCup 2009
Robotic Urban Search and Rescue: A Survey from the Control Perspective
Journal of Intelligent and Robotic Systems
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Recent successful SLAM methods employ hybrid map representations combining the strengths of topological maps and occupancy grids. Such representations often facilitate multi-agent mapping. In this paper, a successful SLAM method is presented, which is inspired by the manifolddata structure by Howard et al. This method maintains a graph with sensor observations stored in vertices and pose differences including uncertainty information stored in edges. Through its graph structure, updates are local and can be efficiently communicated to peers. The graph links represent known traversable space, and facilitate tasks like path planning. We demonstrate that our SLAM method produces very detailed maps without sacrificing scalability. The presented method was used by the UvA Rescue Virtual Robots team, which won the Best Mapping Award in the RoboCup Rescue Virtual Robots competition in 2006.