GPU-accelerated progressive Gaussian filtering with applications to extended object tracking

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 speedups over 20 and real-time tracking can nearly be achieved.