Hybrid localization using the hierarchical atlas

— This paper presents a hybrid localization scheme for a mobile robot using the hierarchical atlas. The hierarchical atlas is a map that consists of a higher level topological graph with lower level feature-based metric submaps associated with the graph edges. Our method employs both a discrete Bayes filter and a Kalman filter to localize the robot in the map. This framework accommodates localization in a map with no prior information (global localization) and localization in a map with an incorrect pose estimate (kidnapped robot). Our approach efficiently scales to large environments without sacrificing accuracy or robustness. We have verified our method with large-scale experiments in a multi-floor office environment.

• Type: Conference
• Year: 2007
• Where: IROS
• Authors: Stephen Tully, Hyungpil Moon, Deryck Morales, George Kantor, Howie Choset
• Keywords: Framework Accommodates Localization, Hierarchical Atlas, Hybrid Localization Scheme, IROS 2007, Robotics