2D Particle Filter Accelerator for Mobile Robot Indoor Localization and Pose Estimation
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 computationall...
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2024-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10418505/ |
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| author | Omer Tariq Dongsoo Han |
| author_facet | Omer Tariq Dongsoo Han |
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| description | 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<inline-formula> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula>, 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. |
| format | Article |
| id | doaj-art-a3c063673e404cb8b90efee95c7d44f9 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a3c063673e404cb8b90efee95c7d44f92025-08-20T03:27:24ZengIEEEIEEE Access2169-35362024-01-0112184731848710.1109/ACCESS.2024.3360883104185052D Particle Filter Accelerator for Mobile Robot Indoor Localization and Pose EstimationOmer Tariq0https://orcid.org/0000-0002-1771-6166Dongsoo Han1School of Computing, Korea Advanced Institute of Science and Technology (KAIST), Yuseong, Daejeon, South KoreaSchool of Computing, Korea Advanced Institute of Science and Technology (KAIST), Yuseong, Daejeon, South KoreaParticle 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<inline-formula> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula>, 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.https://ieeexplore.ieee.org/document/10418505/Pose estimationparticle filter (PF)mobile roboticslocalizationpseudorandom number generator (PRNG)cellular automata |
| spellingShingle | Omer Tariq Dongsoo Han 2D Particle Filter Accelerator for Mobile Robot Indoor Localization and Pose Estimation IEEE Access Pose estimation particle filter (PF) mobile robotics localization pseudorandom number generator (PRNG) cellular automata |
| title | 2D Particle Filter Accelerator for Mobile Robot Indoor Localization and Pose Estimation |
| title_full | 2D Particle Filter Accelerator for Mobile Robot Indoor Localization and Pose Estimation |
| title_fullStr | 2D Particle Filter Accelerator for Mobile Robot Indoor Localization and Pose Estimation |
| title_full_unstemmed | 2D Particle Filter Accelerator for Mobile Robot Indoor Localization and Pose Estimation |
| title_short | 2D Particle Filter Accelerator for Mobile Robot Indoor Localization and Pose Estimation |
| title_sort | 2d particle filter accelerator for mobile robot indoor localization and pose estimation |
| topic | Pose estimation particle filter (PF) mobile robotics localization pseudorandom number generator (PRNG) cellular automata |
| url | https://ieeexplore.ieee.org/document/10418505/ |
| work_keys_str_mv | AT omertariq 2dparticlefilteracceleratorformobilerobotindoorlocalizationandposeestimation AT dongsoohan 2dparticlefilteracceleratorformobilerobotindoorlocalizationandposeestimation |