Genetic Bayesian ARAM for Simultaneous Localization and Hybrid Map Building

This paper presents a new framework for mobile robot to perform localization and build topological-metric hybrid map simultaneously. The proposed framework termed as Genetic Bayesian ARAM consists of two main components: 1) Steady state genetic algorithm (SSGA) for self-localization and occupancy grid map building and 2) Bayesian Adaptive Resonance Associative Memory (ARAM) for topological map building. The proposed method is validated using a mobile robot. Result show that Genetic Bayesian ARAM capable of generate hybrid map online and perform localization simultaneously.

[1]  Kurt Konolige,et al.  Incremental mapping of large cyclic environments , 1999, Proceedings 1999 IEEE International Symposium on Computational Intelligence in Robotics and Automation. CIRA'99 (Cat. No.99EX375).

[2]  Jean-Paul Laumond,et al.  Position referencing and consistent world modeling for mobile robots , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[3]  José del R. Millán,et al.  Efficient learning of variable-resolution cognitive maps for autonomous indoor navigation , 1999, IEEE Trans. Robotics Autom..

[4]  Wan Kyun Chung,et al.  Effective Maximum Likelihood Grid Map With Conflict Evaluation Filter Using Sonar Sensors , 2009, IEEE Transactions on Robotics.

[5]  Naoyuki Kubota,et al.  Evolutionary Computation for Intelligent Self-localization in Multiple Mobile Robots Based on SLAM , 2012, ICIRA.

[6]  Wolfram Burgard,et al.  Map learning and high-speed navigation in RHINO , 1998 .

[7]  Naoyuki Kubota,et al.  Multi-channel Bayesian adaptive resonance associative memory for environment learning and topological map building , 2015, 2015 International Conference on Informatics, Electronics & Vision (ICIEV).

[8]  Jean-Arcady Meyer,et al.  Map-based navigation in mobile robots: I. A review of localization strategies , 2003, Cognitive Systems Research.

[9]  P. Newman,et al.  SLAM in large-scale cyclic environments using the Atlas framework , 2003 .

[10]  Wolfram Burgard,et al.  Integrating Topological and Metric Maps for Mobile Robot Navigation: A Statistical Approach , 1998, AAAI/IAAI.

[11]  Michael Bosse,et al.  Simultaneous Localization and Map Building in Large-Scale Cyclic Environments Using the Atlas Framework , 2004, Int. J. Robotics Res..

[12]  Sebastian Thrun,et al.  Learning Occupancy Grid Maps with Forward Sensor Models , 2003, Auton. Robots.

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

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