scholarly journals Distributed SLAM Using Improved Particle Filter for Mobile Robot Localization

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Fujun Pei ◽  
Mei Wu ◽  
Simin Zhang
Keyword(s):  
Particle Filter ◽  
Information Fusion ◽  
Computation Time ◽  
Computational Process ◽  
Convergence Results ◽  
Importance Function ◽  
Mobile Robot Localization ◽  
Local Filters ◽  
Particle Impoverishment ◽  
Number Of Particles

The distributed SLAM system has a similar estimation performance and requires only one-fifth of the computation time compared with centralized particle filter. However, particle impoverishment is inevitably because of the random particles prediction and resampling applied in generic particle filter, especially in SLAM problem that involves a large number of dimensions. In this paper, particle filter use in distributed SLAM was improved in two aspects. First, we improved the important function of the local filters in particle filter. The adaptive values were used to replace a set of constants in the computational process of importance function, which improved the robustness of the particle filter. Second, an information fusion method was proposed by mixing the innovation method and the number of effective particles method, which combined the advantages of these two methods. And this paper extends the previously known convergence results for particle filter to prove that improved particle filter converges to the optimal filter in mean square as the number of particles goes to infinity. The experiment results show that the proposed algorithm improved the virtue of the DPF-SLAM system in isolate faults and enabled the system to have a better tolerance and robustness.

scholarly journals Alternating Asymmetric Iterative Algorithm Based on Domain Decomposition for 3D Poisson Problem

Mathematics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 281
Author(s):  
Qiuyan Xu ◽  
Zhiyong Liu
Keyword(s):  
Domain Decomposition ◽  
Iterative Algorithm ◽  
Poisson Equation ◽  
Large Scale ◽  
Linear Equations ◽  
Iterative Algorithms ◽  
Computation Time ◽  
Explicit Method ◽  
Computational Process ◽  
Poisson Problem

Poisson equation is a widely used partial differential equation. It is very important to study its numerical solution. Based on the strategy of domain decomposition, the alternating asymmetric iterative algorithm for 3D Poisson equation is provided. The solution domain is divided into several sub-domains, and eight asymmetric iterative schemes with the relaxation factor for 3D Poisson equation are constructed. When the numbers of iteration are odd or even, the computational process of the presented iterative algorithm are proposed respectively. In the calculation of the inner interfaces, the group explicit method is used, which makes the algorithm to be performed fast and in parallel, and avoids the difficulty of solving large-scale linear equations. Furthermore, the convergence of the algorithm is analyzed theoretically. Finally, by comparing with the numerical experimental results of Jacobi and Gauss Seidel iterative algorithms, it is shown that the alternating asymmetric iterative algorithm based on domain decomposition has shorter computation time, fewer iteration numbers and good parallelism.

scholarly journals Mobile Robot Localization Based on Gradient Propagation Particle Filter Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 188475-188487
Author(s):  
Heng Zhang ◽  
Jiemao Wen ◽  
Yanli Liu ◽  
Wenqing Luo ◽  
Naixue Xiong
Keyword(s):  
Mobile Robot ◽  
Particle Filter ◽  
Robot Localization ◽  
Mobile Robot Localization

Quasi-Monte Carlo Gaussian Particle Filtering Acceleration Using CUDA

2011 ◽  
Vol 130-134 ◽  
pp. 3311-3315
Author(s):  
Nai Gao Jin ◽  
Fei Mo Li ◽  
Zhao Xing Li
Keyword(s):  
Monte Carlo ◽  
Particle Filter ◽  
Particle Filtering ◽  
Parallel Implementation ◽  
Accurate Estimation ◽  
Quasi Monte Carlo ◽  
Speedup Ratio ◽  
Non Gaussian ◽  
Gpu Implementation ◽  
Number Of Particles

A CUDA accelerated Quasi-Monte Carlo Gaussian particle filter (QMC-GPF) is proposed to deal with real-time non-linear non-Gaussian problems. GPF is especially suitable for parallel implementation as a result of the elimination of resampling step. QMC-GPF is an efficient counterpart of GPF using QMC sampling method instead of MC. Since particles generated by QMC method provides the best-possible distribution in the sampling space, QMC-GPF can make more accurate estimation with the same number of particles compared with traditional particle filter. Experimental results show that our GPU implementation of QMC-GPF can achieve the maximum speedup ratio of 95 on NVIDIA GeForce GTX 460.

scholarly journals Pedestrian Positioning Using a Double-Stacked Particle Filter in Indoor Wireless Networks

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3907 ◽  
Author(s):  
Kwangjae Sung ◽  
Hyung Kyu Lee ◽  
Hwangnam Kim
Keyword(s):  
Particle Filter ◽  
Microelectromechanical Systems ◽  
Computational Cost ◽  
Dead Reckoning ◽  
Computation Time ◽  
Multipath Fading ◽  
Indoor Environments ◽  
Mems Devices ◽  
Proposal Distribution ◽  
Localization Scheme

The indoor pedestrian positioning methods are affected by substantial bias and errors because of the use of cheap microelectromechanical systems (MEMS) devices (e.g., gyroscope and accelerometer) and the users’ movements. Moreover, because radio-frequency (RF) signal values are changed drastically due to multipath fading and obstruction, the performance of RF-based localization systems may deteriorate in practice. To deal with this problem, various indoor localization methods that integrate the positional information gained from received signal strength (RSS) fingerprinting scheme and the motion of the user inferred by dead reckoning (DR) approach via Bayes filters have been suggested to accomplish more accurate localization results indoors. Among the Bayes filters, while the particle filter (PF) can offer the most accurate positioning performance, it may require substantial computation time due to use of many samples (particles) for high positioning accuracy. This paper introduces a pedestrian localization scheme performed on a mobile phone that leverages the RSS fingerprint-based method, dead reckoning (DR), and improved PF called a double-stacked particle filter (DSPF) in indoor environments. As a key element of our system, the DSPF algorithm is employed to correct the position of the user by fusing noisy location data gained by the RSS fingerprinting and DR schemes. By estimating the position of the user through the proposal distribution and target distribution obtained from multiple measurements, the DSPF method can offer better localization results compared to the Kalman filtering-based methods, and it can achieve competitive localization accuracy compared with PF while offering higher computational efficiency than PF. Experimental results demonstrate that the DSPF algorithm can achieve accurate and reliable localization with higher efficiency in computational cost compared with PF in indoor environments.

scholarly journals Depth and thermal information fusion for head tracking using particle filter in a fall detection context

2020 ◽  
Vol 27 (2) ◽  
pp. 195-208
Author(s):  
Imen Halima ◽  
Jean-Marc Laferté ◽  
Geoffroy Cormier ◽  
Alain-Jérôme Fougères ◽  
Jean-Louis Dillenseger
Keyword(s):  
Particle Filter ◽  
Information Fusion ◽  
Fall Detection ◽  
Head Tracking

scholarly journals A uniformly convergent adaptive particle filter

2005 ◽  
Vol 42 (4) ◽  
pp. 1053-1068 ◽  
Author(s):  
Anastasia Papavasiliou
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Particle Filter ◽  
Adaptive Estimation ◽  
Optimal Filter ◽  
Simultaneous Estimation ◽  
Unknown Parameters ◽  
Partially Observed ◽  
Uniformly Convergent ◽  
Number Of Particles

Particle filters are Monte Carlo methods that aim to approximate the optimal filter of a partially observed Markov chain. In this paper, we study the case in which the transition kernel of the Markov chain depends on unknown parameters: we construct a particle filter for the simultaneous estimation of the parameter and the partially observed Markov chain (adaptive estimation) and we prove the convergence of this filter to the correct optimal filter, as time and the number of particles go to infinity. The filter presented here generalizes Del Moral's Monte Carlo particle filter.

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