Improbability Filtering for Rejecting False Positives

—In this paper we describe a novel approach, called improbability filtering, to rejecting false-positive observations from degrading the tracking performance of an Extended Kalman-Bucy filter. False-positives, incorrect observations reported with a high confidence, are a form of non-Gaussian white noise and therefore degrade 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 Extendend Kalman-Bucy filters for tracking ten robots and a ball moving at speeds approaching 5 m.s-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 b...

• Type: Conference
• Year: 2002
• Where: ICRA
• Authors: Brett Browning, Michael H. Bowling, Manuela M. Veloso
• Keywords: Extended Kalman-bucy Filter, ICRA 2002, Improbability, Kalman-bucy Filters, Robotics