The hierarchical atlas

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.

[1]  Peter Cheeseman,et al.  On the Representation and Estimation of Spatial Uncertainty , 1986 .

[2]  John F. Canny,et al.  Constructing Roadmaps of Semi-Algebraic Sets I: Completeness , 1988, Artificial Intelligence.

[3]  Randall Smith,et al.  Estimating Uncertain Spatial Relationships in Robotics , 1987, Autonomous Robot Vehicles.

[4]  Hugh F. Durrant-Whyte,et al.  Simultaneous map building and localization for an autonomous mobile robot , 1991, Proceedings IROS '91:IEEE/RSJ International Workshop on Intelligent Robots and Systems '91.

[5]  Benjamin Kuipers,et al.  A robot exploration and mapping strategy based on a semantic hierarchy of spatial representations , 1991, Robotics Auton. Syst..

[6]  Maja J. Mataric,et al.  Integration of representation into goal-driven behavior-based robots , 1992, IEEE Trans. Robotics Autom..

[7]  Howie Choset,et al.  Sensor based motion planning: the hierarchical generalized Voronoi graph , 1996 .

[8]  Wolfram Burgard,et al.  A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots , 1998, Auton. Robots.

[9]  Sebastian Thrun,et al.  Learning Metric-Topological Maps for Indoor Mobile Robot Navigation , 1998, Artif. Intell..

[10]  Lindsay Kleeman,et al.  Large Scale Sonarray Mapping using Multiple Connected Local Maps , 1998 .

[11]  Gregory Dudek,et al.  A global topological map formed by local metric maps , 1998, Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No.98CH36190).

[12]  H. Choset,et al.  Toward robust sensor based exploration by constructing reduced generalized Voronoi graph , 1999, Proceedings 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human and Environment Friendly Robots with High Intelligence and Emotional Quotients (Cat. No.99CH36289).

[13]  Yunhui Liu,et al.  Qualitative test and force optimization of 3-D frictional form-closure grasps using linear programming , 1998, IEEE Trans. Robotics Autom..

[14]  Howie Choset,et al.  Sensor-Based Exploration: The Hierarchical Generalized Voronoi Graph , 2000, Int. J. Robotics Res..

[15]  Stergios I. Roumeliotis,et al.  Bayesian estimation and Kalman filtering: a unified framework for mobile robot localization , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[16]  Howie Choset,et al.  Sensor-Based Exploration: Incremental Construction of the Hierarchical Generalized Voronoi Graph , 2000, Int. J. Robotics Res..

[17]  Hugh F. Durrant-Whyte,et al.  Closed form solutions to the multiple-platform simultaneous localization and map building (SLAM) problem , 2000, SPIE Defense + Commercial Sensing.

[18]  Jeffrey K. Uhlmann,et al.  Simultaneous localisation and map building using split covariance intersection , 2001, Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180).

[19]  Keiji Nagatani,et al.  Topological simultaneous localization and mapping (SLAM): toward exact localization without explicit localization , 2001, IEEE Trans. Robotics Autom..

[20]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[21]  Hugh F. Durrant-Whyte,et al.  A solution to the simultaneous localization and map building (SLAM) problem , 2001, IEEE Trans. Robotics Autom..

[22]  Sebastian Thrun,et al.  FastSLAM: a factored solution to the simultaneous localization and mapping problem , 2002, AAAI/IAAI.

[23]  Hugh F. Durrant-Whyte,et al.  Simultaneous Mapping and Localization with Sparse Extended Information Filters: Theory and Initial Results , 2004, WAFR.

[24]  Michael Bosse,et al.  An Atlas framework for scalable mapping , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[25]  Howie Choset,et al.  Hierarchical simultaneous localization and mapping , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[26]  Hugh F. Durrant-Whyte,et al.  Simultaneous Localization and Mapping with Sparse Extended Information Filters , 2004, Int. J. Robotics Res..

[27]  Benjamin Kuipers,et al.  Local metrical and global topological maps in the hybrid spatial semantic hierarchy , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.