An Improved Particle Filter SLAM Algorithm in Similar Environments

2014 ◽  
Vol 590 ◽  
pp. 677-682 ◽  
Author(s):  
Guo Liang Zhang ◽  
Er Liang Yao ◽  
Wen Jun Tang ◽  
Jun Xu
Keyword(s):  
Particle Filter ◽  
Particle Filtering ◽  
Laser Scanning ◽  
Posterior Probability ◽  
Proposal Distribution ◽  
Posterior Probability Distribution ◽  
Maximum Value ◽  
Localization And Mapping ◽  
Slam Algorithm ◽  
Number Of Particles

Because of the challenging data association in similar environments, a large number of particles are needed to improve the precision in particle filtering SLAM (simultaneous localization and mapping).An improved particle filter SLAM algorithm based on particle swarm optimization in similar environments is proposed. A multimode proposal distribution is acquired by combining the information of the odometry and the laser scanning. Particles are concentrated to the region of each posterior probability distribution maximum value by PSO. The performance of the conventional particle filter SLAM is improved. The simulation experiment results prove its effectiveness and feasibility.

Simultaneous Localization and Mapping for Robot Based Multi-Agent System

2014 ◽  
Vol 556-562 ◽  
pp. 2248-2251
Author(s):  
Xiao Kun Leng ◽  
Xin Wei Wang ◽  
Song Hao Piao
Keyword(s):  
Particle Filtering ◽  
Position Estimation ◽  
Multi Agent System ◽  
Agent System ◽  
Localization And Mapping ◽  
Improvement Measures ◽  
Multi Agent ◽  
Accurate Position ◽  
Slam Algorithm ◽  
Number Of Particles

Applications on Multi-agent system have been widely studied recently. The positioning of Robotic system is to estimate the position and posture and accurate position estimation. FastSLAM is a SLAM algorithm based on particle filtering, which can perform positioning fast and has been widely applied. This paper applied the genetic particle filtering into SLAM problem to optimize the SLAM algorithm. We present the algorithms based on genetic particle filtering which can obviously reduce the number of particles needed in FastSLAM. The experimental results show that the improvement measures can effectively improve the performance of the algorithm, so that it enables them to maintain a reliable positioning.

scholarly journals Distributed simultaneous localization and mapping for mobile robot networks via hybrid dynamic belief propagation

2017 ◽  
Vol 13 (8) ◽  
pp. 155014771772671
Author(s):  
Jiuqing Wan ◽  
Shaocong Bu ◽  
Jinsong Yu ◽  
Liping Zhong
Keyword(s):  
Mobile Robot ◽  
Posterior Probability ◽  
Belief Propagation ◽  
Nonlinear Models ◽  
Simultaneous Localization And Mapping ◽  
Factor Graph ◽  
Posterior Probability Distribution ◽  
Localization And Mapping ◽  
Nonparametric Techniques ◽  
Non Gaussian

This article proposes a hybrid dynamic belief propagation for simultaneous localization and mapping in the mobile robot network. The positions of landmarks and the poses of moving robots at each time slot are estimated simultaneously in an online and distributed manner, by fusing the odometry data of each robot and the measurements of robot–robot or robot–landmark relative distance and angle. The joint belief state of all robots and landmarks is encoded by a factor graph and the marginal posterior probability distribution of each variable is inferred by belief propagation. We show how to calculate, broadcast, and update messages between neighboring nodes in the factor graph. Specifically, we combine parametric and nonparametric techniques to tackle the problem arisen from non-Gaussian distributions and nonlinear models. Simulation and experimental results on publicly available dataset show the validity of our algorithm.

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 Global Vision Localization of Indoor Service Robot Based on Improved Iterative Extended Kalman Particle Filter Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Bingshan Hu ◽  
Qingxiao Yu ◽  
Hongliu Yu
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Real Time ◽  
Posterior Probability ◽  
Mobile Robot Navigation ◽  
Estimation Accuracy ◽  
Service Robot ◽  
Mobile Service ◽  
Posterior Probability Distribution ◽  
Particle Filter Algorithm

Localization is the primary problem of mobile robot navigation. Monte Carlo localization based on particle filter has better accuracy and is easier to implement, but there is also the problem of particle degradation. In this paper, the iterative extended Kalman filter is optimized by the Levenberg-Marquardt optimization method. An improved particle filter algorithm based on the upon optimized iterative Kalman filter is proposed, and the importance probability density function of the particle filter is generated by the maximum posterior probability estimation of the improved iterative Kalman filter. Simulation results of the improved particle filter algorithm show that the algorithm can approximate the state posterior probability distribution more closely with fewer sampled particles under the premise of ensuring sufficient state estimation accuracy. Meanwhile, the computation is reduced and the real-time performance is enhanced. Finally, the algorithm is validated on the indoor mobile service robot. The experimental results show that the localization algorithm’s accuracy meets requirement for real-time localizing of the restaurant service robot.

A scan matching simultaneous localization and mapping algorithm based on particle filter

2016 ◽  
Vol 43 (6) ◽  
pp. 607-616 ◽  
Author(s):  
Hui Xiong ◽  
Youping Chen ◽  
Xiaoping Li ◽  
Bing Chen ◽  
Jun Zhang
Keyword(s):  
Particle Filter ◽  
Hill Climbing ◽  
Closure Problem ◽  
Memory Consumption ◽  
Scan Matching ◽  
Loop Closure ◽  
Content Type ◽  
Grid Map ◽  
Localization And Mapping ◽  
Slam Algorithm

Purpose The purpose of this paper is to present a scan matching simultaneous localization and mapping (SLAM) algorithm based on particle filter to generate the grid map online. It mainly focuses on reducing the memory consumption and alleviating the loop closure problem. Design/methodology/approach The proposed method alleviates the loop closure problem by improving the accuracy of the robot’s pose. First, two improvements were applied to enhance the accuracy of the hill climbing scan matching. Second, a particle filter was used to maintain the diversity of the robot’s pose and then to supply potential seeds to the hill climbing scan matching to ensure that the best match point was the global optimum. The proposed method reduces the memory consumption by maintaining only a single grid map. Findings Simulation and experimental results have proved that this method can build a consistent map of a complex environment. Meanwhile, it reduced the memory consumption and alleviates the loop closure problem. Originality/value In this paper, a new SLAM algorithm has been proposed. It can reduce the memory consumption and alleviate the loop closure problem without lowering the accuracy of the generated grid map.

Rao-Blackwellized visual SLAM for small UAVs with vehicle model partition

2014 ◽  
Vol 41 (3) ◽  
pp. 266-274 ◽  
Author(s):  
Tianmiao Wang ◽  
Chaolei Wang ◽  
Jianhong Liang ◽  
Yicheng Zhang
Keyword(s):  
Particle Filter ◽  
Internal State ◽  
Factorization Method ◽  
Measurement Unit ◽  
Visual Slam ◽  
Vehicle Model ◽  
Theoretical Derivation ◽  
Content Type ◽  
Localization And Mapping ◽  
Slam Algorithm

Purpose – The purpose of this paper is to present a Rao–Blackwellized particle filter (RBPF) approach for the visual simultaneous localization and mapping (SLAM) of small unmanned aerial vehicles (UAVs). Design/methodology/approach – Measurements from inertial measurement unit, barometric altimeter and monocular camera are fused to estimate the state of the vehicle while building a feature map. In this SLAM framework, an extra factorization method is proposed to partition the vehicle model into subspaces as the internal and external states. The internal state is estimated by an extended Kalman filter (EKF). A particle filter is employed for the external state estimation and parallel EKFs are for the map management. Findings – Simulation results indicate that the proposed approach is more stable and accurate than other existing marginalized particle filter-based SLAM algorithms. Experiments are also carried out to verify the effectiveness of this SLAM method by comparing with a referential global positioning system/inertial navigation system. Originality/value – The main contribution of this paper is the theoretical derivation and experimental application of the Rao–Blackwellized visual SLAM algorithm with vehicle model partition for small UAVs.

The Research on Chaotic Signal Tracking Algorithm for SR-UKF-PF

2011 ◽  
Vol 69 ◽  
pp. 120-125
Author(s):  
Qian Tao ◽  
Lin Hu Zhu ◽  
Bo Pan
Keyword(s):  
Particle Filter ◽  
Density Function ◽  
Particle Filtering ◽  
State Transition ◽  
Main Idea ◽  
Chaotic Signal ◽  
Tracking Algorithm ◽  
System State ◽  
Proposal Distribution ◽  
Importance Density Function

In view of the difficult in the chaotic signal detection and track in the low signal-to-noise(SNR) environment, a modified SR-UKF-PF is developed that has much better robustness than the traditional SR-UKF and gets almost the same performance as the Particle filter. The main idea of this algorithm is to calculated by the system state transition matrix and the error covariance matrix which are gained from the SR-UKF and the sequential fusion to construct the importance density function of the particle filter. Then the importance density function can integrates the latest observation into system state transition density, and the proposal distribution can approximate the posterior distribution maximumly. To demonstrate the effectiveness of this model, simulations are carried out based on tracking algorithm for the typical chaotic time series of low dimension chaos mapping and super chaos mapping. The simulation results show that this algorithm can overcome the flaw that it is hard to get the optimization importance density function in the particle filter and significantly improves the accuracy of state estimation, and demonstrates the superiorities of particle filtering in the low SNR.

Improved Particle Filter for Integrated Navigation System

2014 ◽  
Vol 543-547 ◽  
pp. 1278-1281 ◽  
Author(s):  
Zhun Jiao ◽  
Rong Zhang
Keyword(s):  
Monte Carlo ◽  
Particle Filter ◽  
Navigation System ◽  
Particle Filtering ◽  
Bayesian Filtering ◽  
Integrated Navigation ◽  
Proposal Distribution ◽  
Gaussian Distributions ◽  
Integrated Navigation System ◽  
Non Gaussian

As a new method for dealing with any nonlinear or non-Gaussian distributions, based on the Monte Carlo methods and Bayesian filtering, particle filters (PF) are favored by researchers and widely applied in many fields. Based on particle filtering, an improved particle filter (IPF) proposal distribution is presented. Evaluation of the weights is simplified and other improved techniques including the residual resampling step and Markov Chain Monte Carlo method are introduced for SINS/GPS integrated navigation system. The simulation results confirm that the improved particle filter outperforms the others.

scholarly journals Stratified Particle Filter Monocular SLAM

2021 ◽  
Vol 13 (16) ◽  
pp. 3233
Author(s):  
Pawel Slowak ◽  
Piotr Kaniewski
Keyword(s):  
Computational Complexity ◽  
Particle Filter ◽  
Real World ◽  
Real World Data ◽  
Localization And Mapping ◽  
Filter Methods ◽  
Monocular Slam ◽  
Importance Factor ◽  
Sample Weighting ◽  
Number Of Particles

This paper presents a solution to the problem of simultaneous localization and mapping (SLAM), developed from a particle filter, utilizing a monocular camera as its main sensor. It implements a novel sample-weighting idea, based on the of sorting of particles into sets and separating those sets with an importance-factor offset. The grouping criteria for samples is the number of landmarks correctly matched by a given particle. This results in the stratification of samples and amplifies weighted differences. The proposed system is designed for a UAV, navigating outdoors, with a downward-pointed camera. To evaluate the proposed method, it is compared with different samples-weighting approaches, using simulated and real-world data. The conducted experiments show that the developed SLAM solution is more accurate and robust than other particle-filter methods, as it allows the employment of a smaller number of particles, lowering the overall computational complexity.

scholarly journals Cooperative simultaneous localization and mapping algorithm based on distributed particle filter

2019 ◽  
Vol 16 (1) ◽  
pp. 172988141881995 ◽  
Author(s):  
Shuhuan Wen ◽  
Jian Chen ◽  
Xiaohan Lv ◽  
Yongzheng Tong
Keyword(s):  
Particle Filter ◽  
Posterior Probability ◽  
Simultaneous Localization And Mapping ◽  
Estimation Algorithm ◽  
Mapping Algorithm ◽  
Global Estimation ◽  
Localization And Mapping ◽  
Mapping System ◽  
Distributed Particle Filter ◽  
Multi Robot

In this article, cooperative simultaneous localization and mapping algorithm based on distributed particle filter is proposed for multi-robot cooperative simultaneous localization and mapping system. First, a multi-robot cooperative simultaneous localization and mapping system model is established based on Rao-Blackwellised particle filter and simultaneous localization and mapping (FastSLAM 2.0) algorithm, and an median of the local posterior probability (MP)-cooperative simultaneous localization and mapping algorithm combined with the M-posterior distributed estimation algorithm is proposed. Then, according to the accuracy advantage of the early landmarks comparing to the later landmarks in the simultaneous localization and mapping task, an improved time-median of the local posterior probability (MP)-cooperative simultaneous localization and mapping algorithm based on time difference optimization is proposed, which optimizes the weights of the local estimation and improves the accuracy of the global estimation. The simulation results show that the algorithm is practical and effective.

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