Multisensor fusion for simultaneous localization and map building

This paper describes how multisensor fusion increases both reliability and precision of the environmental observations used for the simultaneous localization and map-building problem for mobile robots. Multisensor fusion is performed at the level of landmarks, which represent sets of related and possibly correlated sensor observations. The work emphasizes the idea of partial redundancy due to the different nature of the information provided by different sensors. Experimentation with a mobile robot equipped with a multisensor system composed of a 2D laser rangefinder and a charge coupled device camera is reported.

[1]  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.

[2]  Pierrick Grandjean,et al.  3-D modeling of indoor scenes by fusion of noisy range and stereo data , 1989, Proceedings, 1989 International Conference on Robotics and Automation.

[3]  Hugh F. Durrant-Whyte,et al.  Data association for mobile robot navigation: a graph theoretic approach , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[4]  J. M. M. Montiel,et al.  The SPmap: a probabilistic framework for simultaneous localization and map building , 1999, IEEE Trans. Robotics Autom..

[5]  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).

[6]  Peter Cheeseman,et al.  A stochastic map for uncertain spatial relationships , 1988 .

[7]  Raja Chatila,et al.  Stochastic multisensory data fusion for mobile robot location and environment modeling , 1989 .

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

[9]  Gregory Dudek,et al.  Just-in-time sensing: efficiently combining sonar and laser range data for exploring unknown worlds , 1996, Proceedings of IEEE International Conference on Robotics and Automation.

[10]  José A. Castellanos,et al.  Mobile Robot Localization and Map Building: A Multisensor Fusion Approach , 2000 .

[11]  E. Nebot,et al.  Simultaneous Localization and Map Building Using Natural features in Outdoor Environments , .

[12]  Roger Y. Tsai,et al.  A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses , 1987, IEEE J. Robotics Autom..

[13]  Y. Bar-Shalom.  Tracking and data association , 1988 .

[14]  Hugh F. Durrant-Whyte,et al.  Sensor Models and Multisensor Integration , 1988, Int. J. Robotics Res..

[15]  John J. Leonard,et al.  Adaptive Mobile Robot Navigation and Mapping , 1999, Int. J. Robotics Res..

[16]  Richard M. Murray,et al.  A motion planner for nonholonomic mobile robots , 1994, IEEE Trans. Robotics Autom..

[17]  Kai-Tai Song,et al.  Environment recognition for a mobile robot using double ultrasonic sensors and a CCD camera , 1994, Proceedings of 1994 IEEE International Conference on MFI '94. Multisensor Fusion and Integration for Intelligent Systems.

[18]  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).

[19]  José A. Castellanos,et al.  Mobile Robot Localization and Map Building , 1999 .