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Using the Condensation Algorithm to Implement Tracking for Mobile Robots

E.B. Meier and F. Ade
Third European Workshop on Advanced Mobile Robots EUROBOT'99


The detection of objects in every frame of a sequence is often not sufficient for scene interpretation. Tracking can increase the robustness, especially when occlusions occur or when objects temporally disappear. The standard approach for tracking is to use a Kalman filter for every object. This, however, requires the use of a high complexity management system to deal with the multiple hypotheses necessary to track all anticipated objects. In this paper we present a stochastic approach which is based on the Condensation algorithm -- conditional density propagation over time -- that is capable of tracking multiple objects with multiple hypotheses in range images. A probability density function describing the likely state of the objects is propagated over time using a dynamic model. The measurements influence the probability function and allow the incorporation of new objects into the tracking scheme. Additionally, the representation of the density function with a fixed number of samples ensures a constant running time per iteration step. Results on different data sources are shown for mobile robot applications.

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  author = {E.B. Meier and F. Ade},
  title = {Using the Condensation Algorithm to Implement Tracking for Mobile Robots},
  booktitle = {Third European Workshop on Advanced Mobile Robots EUROBOT'99 },
  year = {1999},
  pages = {73-80},
  keywords = {range data, matching, tracking, Condensation algorithm, robot vision}