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Efficient Unbiased Tracking of Multiple Dynamic Obstacles Under Large Viewpoint Changes

Efficient Unbiased Tracking of Multiple Dynamic Obstacles Under Large Viewpoint Changes,10.1109/TRO.2010.2085490,IEEE Transactions on Robotics,Isaac M

Efficient Unbiased Tracking of Multiple Dynamic Obstacles Under Large Viewpoint Changes   (Citations: 1)
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A novel-tracking algorithm is presented as a com- putationally feasible, real-time solution to the joint estimation problem of data assignment and dynamic obstacle tracking from a potentially moving robotic platform. The algorithm implements a Rao-Blackwellized particle filter (RBPF) to factorize the joint estimation problem into 1) a data assignment problem solved via particle filter and 2) a multiple dynamic obstacle-tracking problem solved with efficient parametric filters. The parametric filters make use of a new target representation and stable features developed specifically for tracking full-size vehicles in a dense traffic environment. The algorithm is validated in real time, both in controlled experiments with full-size robotic vehicles and on data collected at the 2007 Defense Advanced Research Projects Agency (DARPA) Urban Challenge.
Journal: IEEE Transactions on Robotics - TRob , vol. 27, no. 1, pp. 29-46, 2011
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    • ...Most autonomous vehicles segment sensor data because of the large amount of data, but have done so independently with each sensor [2]...
    • ...This paper is concerned with segmentation of point clouds that have not undergone any temporal filtering at a higher level to ensure uncorrelated measurements that are passed to higher level functions such as stable cluster tracking [2]...

    Jonathan R. Schoenberget al. Segmentation of dense range information in complex urban scenes

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