Cooperative probabilistic state estimation for vision-based autonomous mobile robots

With the services that autonomous robots are to provide becoming more demanding, the states that the robots have to estimate become more complex. We develop and analyze a probabilistic, vision-based state estimation method for individual, autonomous robots. This method enables a team of mobile robots to estimate their joint positions in a known environment and track the positions of autonomously moving objects. The state estimators of different robots cooperate to increase the accuracy and reliability of the estimation process. This cooperation between the robots enables them to track temporarily occluded objects and to faster recover their position after they have lost track of it. The method is empirically validated based on experiments with a team of physical robots.

[1]  Wolfram Burgard,et al.  Experiences with an Interactive Museum Tour-Guide Robot , 1999, Artif. Intell..

[2]  Michael Beetz,et al.  Watch their moves: applying probabilistic multiple object tracking to autonomous robot soccer , 2002, AAAI/IAAI.

[3]  Andreas Zell,et al.  Vision-based localization for mobile robots , 2001, Robotics Auton. Syst..

[4]  Jake K. Aggarwal,et al.  Mobile robot self-location using model-image feature correspondence , 1996, IEEE Trans. Robotics Autom..

[5]  Wolfram Burgard,et al.  Monte Carlo Localization with Mixture Proposal Distribution , 2000, AAAI/IAAI.

[6]  Nassir Navab,et al.  Yet another method for pose estimation: A probabilistic approach using points, lines, and cylinders , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[7]  Andrew L. Rukhin,et al.  Tools for statistical inference , 1991 .

[8]  Donald Reid.  An algorithm for tracking multiple targets , 1978 .

[9]  Ingemar J. Cox,et al.  An Efficient Implementation of Reid's Multiple Hypothesis Tracking Algorithm and Its Evaluation for the Purpose of Visual Tracking , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Wolfram Burgard,et al.  A Probabilistic Approach to Collaborative Multi-Robot Localization , 2000, Auton. Robots.

[11]  Bernhard Nebel,et al.  Cooperative sensing in dynamic environments , 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).

[12]  Ingemar J. Cox,et al.  Modeling a Dynamic Environment Using a Bayesian Multiple Hypothesis Approach , 1994, Artif. Intell..

[13]  Günther Palm,et al.  Soccer-robot localization using sporadic visual features , 2000 .

[14]  Peter Stone,et al.  RoboCup-2000: The Fourth Robotic Soccer World Championships , 2001, AI Mag..

[15]  Jeffrey K. Uhlmann,et al.  New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.

[16]  Stergios I. Roumeliotis,et al.  Collective localization: a distributed Kalman filter approach to localization of groups of mobile robots , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[17]  Wolfram Burgard,et al.  Probabilistic Algorithms and the Interactive Museum Tour-Guide Robot Minerva , 2000, Int. J. Robotics Res..

[18]  O. Faugeras.  Three-dimensional computer vision: a geometric viewpoint , 1993 .

[19]  Jurjen Caarls,et al.  Fast and Accurate Robot Vision for Vision Based Motion , 2000, RoboCup.

[20]  Thorsten Schmitt,et al.  From Multiple Images to a Consistent View , 2000, RoboCup.

[21]  Michael Isard,et al.  Active Contours , 2000, Springer London.

[22]  Michael Beetz,et al.  Cooperative probabilistic state estimation for vision-based autonomous mobile robots , 2002, IEEE Trans. Robotics Autom..

[23]  Bernhard Nebel,et al.  The CS Freiburg Robotic Soccer Team: Reliable Self-Localization, Multirobot Sensor Integration, and Basic Soccer Skills , 1998, RoboCup.

[24]  Ryo Kurazume,et al.  Study on cooperative positioning system: optimum moving strategies for CPS-III , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[25]  Patric Jensfelt,et al.  Active global localization for a mobile robot using multiple hypothesis tracking , 2001, IEEE Trans. Robotics Autom..

[26]  Daniele Nardi,et al.  Self-Localization in the RoboCup Environment , 1999, RoboCup.

[27]  Wolfram Burgard,et al.  Tracking multiple moving targets with a mobile robot using particle filters and statistical data association , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[28]  Ernst D. Dickmanns,et al.  Vehicles Capable of Dynamic Vision , 1997, IJCAI.

[29]  P. Pérez,et al.  Tracking multiple objects with particle filtering , 2002 .

[30]  H. Durrant-Whyte,et al.  Mobile vehicle navigation in unknown environments: a multiple hypothesis approach , 1995 .

[31]  Michael Beetz,et al.  The AGILO autonomous robot soccer team: computational principles, experiences, and perspectives , 2002, AAMAS '02.

[32]  Pedro U. Lima,et al.  Vision-based self-localization for soccer robots , 2000, Proceedings. 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000) (Cat. No.00CH37113).

[33]  W. Burgard,et al.  Markov Localization for Mobile Robots in Dynamic Environments , 1999, J. Artif. Intell. Res..

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

[35]  Sebastian Thrun,et al.  Probabilistic Algorithms in Robotics , 2000, AI Mag..

[36]  Karen Zita Haigh,et al.  Xavier: experience with a layered robot architecture , 1997, SGAR.

[37]  Thorsten Schmitt,et al.  Vision-based localization and data fusion in a system of cooperating mobile robots , 2000, Proceedings. 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000) (Cat. No.00CH37113).

[38]  William H. Press,et al.  Numerical recipes in C , 2002 .