Improbability filtering for rejecting false positives

We describe an approach, called improbability filtering, to rejecting false-positive observations from degrading the tracking performance of an extended Kalman-Bucy filter. Improbability filtering removes false-positives by rejecting low likelihood observations as determined by the model estimates. It offers a computationally fast and robust method for removing this form of white noise without the need for a more advanced filter. We describe an application of the improbability filter approach to extended Kalman-Bucy filters for tracking ten robots and a ball moving at speeds approaching 5 m s/sup -1/ both accurately and reliably in real-time based on the observations of a single color camera. The environment is highly dynamic and non-linear, as exemplified by the motion of the ball which varies from free rolling under friction, to roiling up 45/spl deg/ inclined walls at the boundary, to being manipulated in unpredictable ways by a mechanical apparatus on each robot. The sensing apparatus, a camera and color blob tracking algorithms, suffers from the usual noise, latency, intermittency, as well as from false-positives caused by the misidentification of an observed object with a nonnegligible likelihood.

[1]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[2]  N E Manos,et al.  Stochastic Models , 1960, Encyclopedia of Social Network Analysis and Mining. 2nd Ed..

[3]  R. Kalman,et al.  New results in linear prediction and filtering theory Trans. AMSE , 1961 .

[4]  S. Abbott The National Museum of American History , 1981 .

[5]  Guanrong Chen,et al.  Kalman Filtering with Real-time Applications , 1987 .

[6]  Greg Welch,et al.  Welch & Bishop , An Introduction to the Kalman Filter 2 1 The Discrete Kalman Filter In 1960 , 1994 .

[7]  Hiroaki Kitano,et al.  RoboCup: A Challenge Problem for AI and Robotics , 1997, RoboCup.

[8]  Manuela M. Veloso,et al.  Reactive visual control of multiple non-holonomic robotic agents , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[9]  E. Kamen,et al.  Introduction to Optimal Estimation , 1999 .

[10]  Manuela Veloso,et al.  An Analysis of Stochastic Game Theory for Multiagent Reinforcement Learning , 2000 .

[11]  M. Veloso,et al.  Rational Learning of Mixed Equilibria in Stochastic Games , 2000 .

[12]  Manuela M. Veloso,et al.  Fast and inexpensive color image segmentation for interactive robots , 2000, Proceedings. 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000) (Cat. No.00CH37113).

[13]  Brett Browning,et al.  CM-Dragons'01 - Vision-Based Motion Tracking and Heteregenous Robots , 2001, RoboCup.

[14]  Michael Beetz,et al.  Plan-Based Control of Robotic Agents , 2002, Lecture Notes in Computer Science.

[15]  Andreas Birk,et al.  RoboCup 2001: Robot Soccer World Cup V , 2002, Lecture Notes in Computer Science.

[16]  Manuela Veloso,et al.  Multiagent learning in the presence of agents with limitations , 2003 .