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Exploiting Clutter: Negative Information for Enhanced Extended Object Tracking

Zea, A.; Faion, F.; Hanebeck, U.D.

Since the last years, Graphics Processing Units (GPUs) have massive parallel execution capabilities even
for non-graphic related applications. The field of nonlinear state estimation is no exception here.
Particle Filters have already been successfully ported to GPUs. In this paper,
we propose a GPU-accelerated variant of the Progressive Gaussian Filter (PGF). This allows
us to combine the advantages of the particle flow with the ability to process thousands of
measurements at once in order to improve state estimation quality. To get a meaningful
comparison between its CPU and GPU variants, we additionally propose a likelihood for tracking
a sphere and its extent in 3D based on noisy point measurements. The likelihood considers the
physical relationship between sensor, measurement, and sphere to best exploit the information of the received measurements.
We evaluate the GPU implementation of the PGF using the proposed likelihood in combination with tens of thousands of measurements.
Although the CPU implementation fully exploits parallelization techniques such as SSE and OpenMP, the GPU-accelerated PGF
reaches spee ... mehr

Zugehörige Institution(en) am KIT Institut für Anthropomatik und Robotik (IAR)
Publikationstyp Proceedingsbeitrag
Jahr 2015
Sprache Englisch
Identifikator ISBN: 978-0-9824-4386-6
KITopen ID: 1000050855
Erschienen in Proceedings of the 18th International Conference on Information Fusion (Fusion 2015), 6-9 July 2015, Washington D. C., USA
Verlag IEEE, Piscataway (NJ)
Seiten 1030-1037
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