DC Field | Value | Language |
---|---|---|
dc.contributor.author | Tariq, Omer | ko |
dc.contributor.author | Han, Dong-Soo | ko |
dc.date.accessioned | 2024-08-27T12:00:08Z | - |
dc.date.available | 2024-08-27T12:00:08Z | - |
dc.date.created | 2024-07-10 | - |
dc.date.issued | 2024 | - |
dc.identifier.citation | IEEE ACCESS, v.12, pp.18473 - 18487 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | http://hdl.handle.net/10203/322436 | - |
dc.description.abstract | Particle filtering is a reliable Monte Carlo algorithm for estimating the state of a system in modeling non-linear, non-gaussian elements for estimation and tracking applications in various fields, including robotics, navigation, and computer vision. However, particle filtering can be computationally expensive, particularly in high-dimensional state spaces, and can be a bottleneck for real-time applications due to high memory consumption. This paper proposes a particle filter accelerator that employs a cellular automata-based pseudo-random number generator and an improved systematic resampler based on the Vose Alias method. The particles are distributed across several sub-filters, performing concurrent resampling and importance weights computations. The proposed accelerator leveraged the inherent parallelism and pipelining stages of FPGAs to perform the resampling stage in a parallel fashion, significantly enhancing the particle convergence time. The proposed accelerator deployed on the Zedboard (ZC7020) system-on-chip achieves a low execution time of approximately 4.63 $\mu \text{s}$ , 21.3 % speedup, and 3.1 % area reduction compared to the recent particle filter accelerator. The proposed design also demonstrates modularity, achieved through multiple parallel hardware subfilters that provide high throughput for real-time sensor data processing. Furthermore, the proposed accelerator performs a high sampling frequency of 216kHz, making it suitable for high throughput and real-time applications. | - |
dc.language | English | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | 2D Particle Filter Accelerator for Mobile Robot Indoor Localization and Pose Estimation | - |
dc.type | Article | - |
dc.identifier.wosid | 001161840600001 | - |
dc.identifier.scopusid | 2-s2.0-85184337571 | - |
dc.type.rims | ART | - |
dc.citation.volume | 12 | - |
dc.citation.beginningpage | 18473 | - |
dc.citation.endingpage | 18487 | - |
dc.citation.publicationname | IEEE ACCESS | - |
dc.identifier.doi | 10.1109/ACCESS.2024.3360883 | - |
dc.contributor.localauthor | Han, Dong-Soo | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Pose estimation | - |
dc.subject.keywordAuthor | Mobile robots | - |
dc.subject.keywordAuthor | Monte Carlo methods | - |
dc.subject.keywordAuthor | Markov processes | - |
dc.subject.keywordAuthor | particle filter (PF) | - |
dc.subject.keywordAuthor | mobile robotics | - |
dc.subject.keywordAuthor | localization | - |
dc.subject.keywordAuthor | pseudorandom number generator (PRNG) | - |
dc.subject.keywordAuthor | cellular automata | - |
dc.subject.keywordAuthor | field programmable gate arrays (FPGA) | - |
dc.subject.keywordAuthor | very large scale integration (VLSI) | - |
dc.subject.keywordAuthor | Monte Carlo Markov chain (MCMC) | - |
dc.subject.keywordAuthor | sampling importance re-sampling (SIR) | - |
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