Robotics: Science and Systems II

The Identity Management Kalman Filter (IMKF)

B. Schumitsch, S. Thrun, L. Guibas, K. Olukotun

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.

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Bibtex:

@INPROCEEDINGS{ Schumitsch-RSS-06,
    AUTHOR    = {B. Schumitsch and S. Thrun and L. Guibas and K. Olukotun},
    TITLE     = {The Identity Management {Kalman} Filter (IMKF)},
    BOOKTITLE = {Proceedings of Robotics: Science and Systems},
    YEAR      = {2006},
    ADDRESS   = {Philadelphia, USA},
    MONTH     = {August},
    DOI       = {10.15607/RSS.2006.II.029} 
}