Abstract: Particle filters (PFs) are Bayesian based estimation algorithms with attractive theoretical properties for addressingwide range of complex applications that are nonlinear and nonGaussian.
Abstract—Particle filters (PFs) are Bayesian based estimation algorithms with attractive theoretical properties for addressing.
This paper presents different approaches for PFs acceleration based on afield programmable gate arrays (FPGAs) to address such a drawback in PFs. Particle ...
To address such a drawback in PFs, this paperpresents different approaches for PFs acceleration based on afield programmable gate arrays (FPGAs). Accelerating ...
Particle filtering (an application of Monte-Carlo sampling) has been implemented on FPGAs for accelerating object tracking and robot mapping and localization [ ...
A modification to the existing particle filter algorithm is proposed, which enables parallel re-sampling and reduces the effect of the re-Sampling ...
Sep 1, 2020 · FastSLAM [2,3] and GMapping [4] are the most popular methods among particle filter-based approaches and are proven to work well in the ...
Our implementation of particle filters on FPGA is scalable and modular, with a low execution time of about 5.62 µs for processing 1024 particles (compared to 64.
Based on the proposed PF acceleration techniques, hardware blocks are designed to speed up the computational time and interface in a central MicroBlaze soft ...
Duration: 1:26
Posted: Aug 10, 2021
Posted: Aug 10, 2021