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...
Saved in:
| Main Authors: | Omer Tariq, Dongsoo Han |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10418505/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Design and Hardware Implementation of a Highly Flexible PRNG System for NIST-Validated Pseudorandom Sequences
by: María de Lourdes Rivas Becerra, et al.
Published: (2025-05-01) -
A novel approach to pseudorandom number generation using Hamiltonian conservative chaotic systems
by: Vinod Patidar, et al.
Published: (2025-03-01) -
Joint Iterative Satellite Pose Estimation and Particle Swarm Optimization
by: Patcharin Kamsing, et al.
Published: (2025-02-01) -
Consistent multi-animal pose estimation in cattle using dynamic Kalman filter based tracking
by: Maarten Perneel, et al.
Published: (2025-08-01) -
Optimal Synthesis of Pose Repeatability for a Collaborative Robot
by: Xu Liu, et al.
Published: (2021-03-01)