Topologically-directed navigation
Robotica
A Scalable Hybrid Multi-robot SLAM Method for Highly Detailed Maps
RoboCup 2007: Robot Soccer World Cup XI
Robot task planning using semantic maps
Robotics and Autonomous Systems
A multi-hypothesis topological SLAM approach for loop closing on edge-ordered graphs
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Real-time optical SLAM-based mosaicking for unmanned underwater vehicles
Intelligent Service Robotics
A unified Bayesian framework for global localization and SLAM in hybrid metric/topological maps
International Journal of Robotics Research
How the Location of the Range Sensor Affects EKF-based Localization
Journal of Intelligent and Robotic Systems
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This paper presents a new map specifically designed for robots operating in large environments and possibly in higher dimensions. We call this map the hierarchical atlas because it is a multilevel and multiresolution representation. For this paper, the hierarchical atlas has two levels: at the highest level there is a topological map that organizes the free space into submaps at the lower level. The lower-level submaps are simply a collection of features. The hierarchical atlas allows us to perform calculations and run estimation techniques, such as Kalman filtering, in local areas without having to correlate and associate data for the entire map. This provides a means to explore and map large environments in the presence of uncertainty with a process named hierarchical simultaneous localization and mapping. As well as organizing information of the free space, the map also induces well-defined sensor-based control laws and a provably complete policy to explore unknown regions. The resulting map is also useful for other tasks such as navigation, obstacle avoidance, and global localization. Experimental results are presented showing successful map building and subsequent use of the map in large-scale spaces.