Modified particle filter algorithm for mobile robot simultaneous localization and mapping

2009 ◽  
Author(s):  
Wang Zhongmin ◽  
Miao Dehua ◽  
Du Zhijiang
Keyword(s):  
Mobile Robot ◽  
Particle Filter ◽  
Simultaneous Localization And Mapping ◽  
Localization And Mapping ◽  
Particle Filter Algorithm

scholarly journals Grey Wolf Resampling-Based Rao-Blackwellized Particle Filter for Mobile Robot Simultaneous Localization and Mapping

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yong Dai ◽  
Ming Zhao
Keyword(s):  
Mobile Robot ◽  
Particle Filter ◽  
Data Association ◽  
Simultaneous Localization And Mapping ◽  
Fast Convergence ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Local Data ◽  
Localization And Mapping ◽  
Indoor Mobile Robot

An artificial intelligent grey wolf optimizer (GWO)-assisted resampling scheme is applied to the Rao-Blackwellized particle filter (RBPF) in the simultaneous localization and mapping (SLAM). By doing this, we can make the diversity of the particles resampling and then obtain a better localization accuracy and fast convergence to realize indoor mobile robot SLAM. In addition, we propose an adaptive local data association (Range-SLAM) scheme to improve the computational efficiency for the algorithm of the nearest neighbor (NN) data association in the iteration of the RBPF prediction. Through the experiment and simulations, the proposed SLAM schemes have fast convergence, accuracy, and heuristics. Therefore, the improved RBPF and new data association schemes presented in this paper can provide a feasible method for the indoor mobile robot SLAM.

PARTICLE FILTER IN SIMULTANEOUS LOCALIZATION AND MAPPING (SLAM) USING DIFFERENTIAL DRIVE MOBILE ROBOT

2015 ◽  
Vol 77 (20) ◽  
Author(s):  
Norhidayah Mohamad Yatim ◽  
Norlida Buniyamin
Keyword(s):  
Kalman Filter ◽  
Mobile Robot ◽  
Particle Filter ◽  
Mean Squared Error ◽  
Simultaneous Localization And Mapping ◽  
Simulation Accuracy ◽  
Differential Drive ◽  
Estimation Algorithms ◽  
Localization And Mapping ◽  
Number Of Particles

Simultaneous Localization and Mapping (SLAM) problem is a well-known problem in robotics, where a robot has to localize itself and map its environment simultaneously. Particle filter (PF) is one of the most adapted estimation algorithms for SLAM apart from Kalman filter (KF) and Extended Kalman Filter (EKF). In this work, particle filter algorithm has been successfully implemented using a simple differential drive mobile robot called e-puck. The performance of the algorithm implemented is analyzed via varied number of particles. From simulation, accuracy of the resulting maps differed according to the number of particles used. The Root Mean Squared Error (RMSE) of a larger number of particles is smaller compared to a lower number of particles after a period of time.  

scholarly journals SLAM of Mobile Robot for Wireless Communication Based on Improved Particle Filter

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Daixian Zhu ◽  
Mingbo Wang ◽  
Mengyao Su ◽  
Shulin Liu ◽  
Ping Guo
Keyword(s):  
Wireless Communication ◽  
Mobile Robot ◽  
Particle Filter ◽  
Simultaneous Localization And Mapping ◽  
Positioning Accuracy ◽  
Diversity Loss ◽  
Localization And Mapping ◽  
Auxin Content ◽  
Robot Positioning ◽  
Particle Weight

The mobile robot is moved by receiving instructions through wireless communication, and the particle filter is used to simultaneous localization and mapping. Aiming at the problem of the degradation of particle filter weights and loss of particle diversity, which leads to the decrease of filter accuracy, this paper uses the plant cell swarm algorithm to optimize the particle filter. First of all, combining the characteristics of plant cells that affect the growth rate of cells when the auxin content changes due to light stimulation realizes the optimization of the particles after importance sampling, so that they are concentrated in the high-likelihood area, and the problem of particle weight degradation is solved. Secondly, in the process of optimizing particle distribution, the auxin content of each particle is different, which makes the optimization effect on each particle different, so it effectively solves the problem of particle diversity loss. Finally, a simulation experiment is carried out. During the experiment, the robot moves by receiving control commands through wireless communication. The experimental results show that the algorithm effectively solves the problem of particle weight degradation and particle diversity loss and improves the filtering accuracy. The improved algorithm is verified in the simultaneous localization and mapping of the robot, which effectively improves the robot’s performance at the same time positioning accuracy. Compared with the classic algorithm, the robot positioning accuracy is increased by 49.2%. Moreover, the operational stability of the algorithm has also been improved after the improvement.

TSLAM: Tethered simultaneous localization and mapping for mobile robots

2017 ◽  
Vol 36 (12) ◽  
pp. 1363-1386 ◽  
Author(s):  
Patrick McGarey ◽  
Kirk MacTavish ◽  
François Pomerleau ◽  
Timothy D Barfoot
Keyword(s):  
Mobile Robots ◽  
Particle Filter ◽  
Low Cost ◽  
Simultaneous Localization And Mapping ◽  
Ground Truth ◽  
Anchor Point ◽  
Outlier Rejection ◽  
Cluttered Environments ◽  
Localization And Mapping ◽  
Anchor Points

Tethered mobile robots are useful for exploration in steep, rugged, and dangerous terrain. A tether can provide a robot with robust communications, power, and mechanical support, but also constrains motion. In cluttered environments, the tether will wrap around a number of intermediate ‘anchor points’, complicating navigation. We show that by measuring the length of tether deployed and the bearing to the most recent anchor point, we can formulate a tethered simultaneous localization and mapping (TSLAM) problem that allows us to estimate the pose of the robot and the positions of the anchor points, using only low-cost, nonvisual sensors. This information is used by the robot to safely return along an outgoing trajectory while avoiding tether entanglement. We are motivated by TSLAM as a building block to aid conventional, camera, and laser-based approaches to simultaneous localization and mapping (SLAM), which tend to fail in dark and or dusty environments. Unlike conventional range-bearing SLAM, the TSLAM problem must account for the fact that the tether-length measurements are a function of the robot’s pose and all the intermediate anchor-point positions. While this fact has implications on the sparsity that can be exploited in our method, we show that a solution to the TSLAM problem can still be found and formulate two approaches: (i) an online particle filter based on FastSLAM and (ii) an efficient, offline batch solution. We demonstrate that either method outperforms odometry alone, both in simulation and in experiments using our TReX (Tethered Robotic eXplorer) mobile robot operating in flat-indoor and steep-outdoor environments. For the indoor experiment, we compare each method using the same dataset with ground truth, showing that batch TSLAM outperforms particle-filter TSLAM in localization and mapping accuracy, owing to superior anchor-point detection, data association, and outlier rejection.

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