A Probabilistic Approach for Concurrent Map Acquisition and Localization for Mobile Robots

Abstract : This paper addresses the problem of building large scale geometric maps of indoor environments with mobile robots. It poses the map building problem as a constrained, probabilistic maximum likelihood estimation problem. It then devises a practical algorithm for generating the most likely map from data, along with the most likely path taken by the robot. Experimental results in cyclic environments of size up to 80 by 25 meter illustrate the appropriateness of the approach.

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

[2]  Hans P. Moravec.  Sensor Fusion in Certainty Grids for Mobile Robots , 1988, AI Mag..

[3]  Alberto Elfes,et al.  Occupancy grids: a probabilistic framework for robot perception and navigation , 1989 .

[4]  Ingemar J. Cox,et al.  Dynamic Map Building for an Autonomous Mobile Robot , 1990, EEE International Workshop on Intelligent Robots and Systems, Towards a New Frontier of Applications.

[5]  Carl F. R. Weiman,et al.  Helpmate autonomous mobile robot nav-igation system , 1991 .

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

[7]  Wolfgang D. Rencken,et al.  Concurrent localisation and map building for mobile robots using ultrasonic sensors , 1993, Proceedings of 1993 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '93).

[8]  Maja J. Mataric,et al.  Interaction and intelligent behavior , 1994 .

[9]  Evangelos E. Milios,et al.  Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

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

[11]  Wolfram Burgard,et al.  The Mobile Robot Rhino , 1995, SNN Symposium on Neural Networks.

[12]  Lynne E. Parker,et al.  On the design of behavior-based multi-robot teams , 1995, Adv. Robotics.

[13]  Illah R. Nourbakhsh,et al.  DERVISH - An Office-Navigating Robot , 1995, AI Mag..

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

[15]  Sebastian Thrun,et al.  Learning Maps for Indoor Mobile Robot Navigation. , 1996 .

[16]  Wolfram Burgard,et al.  Estimating the Absolute Position of a Mobile Robot Using Position Probability Grids , 1996, AAAI/IAAI, Vol. 2.

[17]  Reid G. Simmons,et al.  Passive Distance Learning for Robot Navigation , 1996, ICML.

[18]  Liqiang Feng,et al.  Navigating Mobile Robots: Systems and Techniques , 1996 .

[19]  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.

[20]  Wolfram Burgard,et al.  Position tracking with position probability grids , 1996, Proceedings of the First Euromicro Workshop on Advanced Mobile Robots (EUROBOT '96).

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

[22]  Evangelos E. Milios,et al.  Globally Consistent Range Scan Alignment for Environment Mapping , 1997, Auton. Robots.

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

[24]  Maja J. Mataric,et al.  Reinforcement Learning in the Multi-Robot Domain , 1997, Auton. Robots.

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

[26]  Wolfram Burgard,et al.  A hybrid collision avoidance method for mobile robots , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[27]  Sebastian Thrun,et al.  A Bayesian Approach to Landmark Discovery and Active Perception in Mobile Robot Navigation , 1999 .