[My talk] The Identity Management Kalman Filter (IMKF)

Title: The Identity Management Kalman Filter (IMKF)

Authors: Brad Schumitsch, Sebastian Thrun, Leonidas Guibas, Kunle Olukotun

Robotics: Science and Systems II (RSS 2006)
August 16-19, 2006
University of Pennsylvania
Philadelphia, Pennsylvania


Abstract: Tracking posteriors estimates for problems with data association uncertainty is one of the big open problems in the literature on filtering and tracking. This paper presents a new filter for online tracking of many individual objects with data association ambiguities. It tightly integrates the continuous aspects of the problem -- locating the objects -- with the discrete aspects -- the data association ambiguity. The key innovation is a probabilistic information matrix that efficiently does identity management, that is, it links entities with internal tracks of the filter, enabling it to maintain a full posterior over the system amid data association uncertainties. The filter scales quadratically in complexity, just like a conventional Kalman filter. We derive the algorithm formally and present large-scale results.

PDF:
http://www.roboticsproceedings.org/rss02/p29.pdf