An Adaptive Unscented Particle Filter Algorithm through Relative Entropy for Mobile Robot Self-Localization

Fuzzy Adaptive Particle Filter for Localization of a Mobile Robot

Localization is one of the important topics in robotics and it is essential to execute a mission. Most problems in the class of localization are due to uncertainties in the modeling and sensors. Therefore, various filters are developed to estimate the states in noisy information. Recently, particle filter is issued widely because it can be applied to a nonlinear model and a non-Gaussian noise. ...

Robust Adaptive Unscented Particle Filter

Unscented Kalman Filter Position Estimation for an Autonomous Mobile Robot

The Kalman filters have been widely used for mobile robot navigation and system integration. So that it may operate autonomously, a mobile robot must know where it is. Accurate localization is a key prerequisite for successful navigation in large-scale environments, particularly when global models are used, such as maps, drawings, topological descriptions, and CAD models. This paper presents th...

Distributed SLAM Using Improved Particle Filter for Mobile Robot Localization

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 f...

A Multi Swarm Particle Filter for Mobile Robot Localization

Particle filter (PF) is widely used in mobile robot localization, since it is suitable for the nonlinear nonGaussian system. Localization based on PF, However, degenerates over time. This degeneracy is due to the fact that a particle set estimating the pose of the robot looses its diversity. One of the main reasons for loosing particle diversity is sample impoverishment. It occurs when likeliho...