Global localization and topological map-learning for robot navigation

This paper describes a navigation system implemented on a real mobile robot. Using simple sonar and visual sensors, it makes possible the autonomous construction of a dense topological map representing the environment. At any time during the mapping process, this system is able to globally localize the robot, i.e. to estimate the robot's position even if the robot is passively moved from one place to another within the mapped area. This is achieved using algorithms inspired by Hidden Markov Models adapted to the on-line building of the map. Advantages and drawbacks of the system are discussed, along with its potential implications for the understanding of biological navigation systems.

[1]  Alexandre Pouget,et al.  Probabilistic Interpretation of Population Codes , 1996, Neural Computation.

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

[3]  Philippe Gaussier,et al.  The visual homing problem: An example of robotics/biology cross fertilization , 2000, Robotics Auton. Syst..

[4]  Gaurav S. Sukhatme,et al.  Incremental online topological map building with a mobile robot , 1999, Optics East.

[5]  Bernhard Schölkopf,et al.  Learning view graphs for robot navigation , 1997, AGENTS '97.

[6]  Alan C. Schultz,et al.  Integrating Exploration and Localization for Mobile Robots , 1999, Adapt. Behav..

[7]  Wolfram Burgard,et al.  A real-time algorithm for mobile robot mapping with applications to multi-robot and 3D mapping , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[8]  Reid G. Simmons,et al.  Probabilistic Robot Navigation in Partially Observable Environments , 1995, IJCAI.

[9]  Leslie Pack Kaelbling,et al.  Acting under uncertainty: discrete Bayesian models for mobile-robot navigation , 1996, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. IROS '96.

[10]  P. Gaussiera,et al.  The visual homing problem : An example of robotics / biology cross fertilization , 1999 .

[11]  Randall D. Beer,et al.  Spatial learning for navigation in dynamic environments , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[12]  E. Tolman.  Cognitive maps in rats and men. , 1948, Psychological review.

[13]  Leslie Pack Kaelbling,et al.  Learning Topological Maps with Weak Local Odometric Information , 1997, IJCAI.

[14]  Jean-Arcady Meyer,et al.  Using Coloured Snapshots For Short-Range Guidance In Mobile Robots , 2002 .

[15]  David Filliat.  Cartographie et estimation globale de la position pour un robot mobile autonome , 2001 .

[16]  Dario Floreano,et al.  From Animals to Animats 2: Proceedings of the Second International Conference on Simulation of Adaptive Behavior , 2000, Journal of Cognitive Neuroscience.

[17]  Vasant Honavar,et al.  Spatial Learning and Localization in Rodents: A Computational Model of the Hippocampus and its Implications for Mobile Robots , 1999, Adapt. Behav..

[18]  Hans P. Moravec,et al.  High resolution maps from wide angle sonar , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[19]  R. Simmons,et al.  Probabilistic Navigation in Partially Observable Environments , 1995 .

[20]  David Filliat,et al.  Map-based navigation in mobile robots: II. A review of map-learning and path-planning strategies , 2003, Cognitive Systems Research.

[21]  B L McNaughton,et al.  Interpreting neuronal population activity by reconstruction: unified framework with application to hippocampal place cells. , 1998, Journal of neurophysiology.

[22]  Peter C. Cheeseman,et al.  Estimating uncertain spatial relationships in robotics , 1986, Proceedings. 1987 IEEE International Conference on Robotics and Automation.

[23]  Wolfram Burgard,et al.  Position Estimation for Mobile Robots in Dynamic Environments , 1998, AAAI/IAAI.

[24]  Jean-Arcady Meyer,et al.  BIOLOGICALLY BASED ARTIFICIAL NAVIGATION SYSTEMS: REVIEW AND PROSPECTS , 1997, Progress in Neurobiology.

[25]  Angelo Arleo,et al.  Spatial cognition and neuro-mimetic navigation: a model of hippocampal place cell activity , 2000, Biological Cybernetics.

[26]  Illah R. Nourbakhsh,et al.  Appearance-based place recognition for topological localization , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[27]  Stephen R. Marsland,et al.  Learning globally consistent maps by relaxation , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[28]  Wolfram Burgard,et al.  MINERVA: a second-generation museum tour-guide robot , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[29]  Jean-Arcady Meyer,et al.  Active Perception and Map Learning for Robot Navigation , 2000 .

[30]  Marek Piasecki,et al.  Global localization for mobile robots by multiple hypothesis tracking , 1995, Robotics Auton. Syst..