Toward Simple Strategy for Optimal Tracking and Localization of Robots With Adaptive Particle Filtering

The ability of robotic systems to autonomously understand and/or navigate in uncertain environments is critically dependent on fairly accurate strategies, which are not always optimally achieved due to effectiveness, computational cost, and parameter settings. In this paper, we propose a novel and s...

Full description

Saved in:
Bibliographic Details
Published in:IEEE/ASME transactions on mechatronics 2016-12, Vol.21 (6), p.2793-2804
Main Authors: de J Mateo Sanguino, Tomas, Ponce Gomez, Francisco
Format: Article
Language:English
Subjects:
Adaptive filters
Adaptive systems
Algorithms
Computing costs
Effectiveness
global localization
IEEE transactions
Kinect
Localization
Mechatronics
mobile robotics
Mobile robots
object tracking
Optimization
Particle filters
Robot sensing systems
Robots
System effectiveness
Tracking
Uncertainty
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The ability of robotic systems to autonomously understand and/or navigate in uncertain environments is critically dependent on fairly accurate strategies, which are not always optimally achieved due to effectiveness, computational cost, and parameter settings. In this paper, we propose a novel and simple adaptive strategy to increase the efficiency and drastically reduce the computational effort in particle filters (PFs). The purpose of the adaptive approach (dispersion-based adaptive particle filter - DAPF) is to provide higher number of particles during the initial searching state (when the localization presents greater uncertainty) and fewer particles during the subsequent state (when the localization exhibits less uncertainty). With the aim of studying the dynamical PF behavior regarding others and putting the proposed algorithm into practice, we designed a methodology based on different target applications and a Kinect sensor. The various experiments conducted for both color tracking and mobile robot localization problems served to demonstrate that the DAPF algorithm can be further generalized. As a result, the DAPF approach significantly improved the computational performance over two well-known filtering strategies: 1) the classical PF with fixed particle set sizes, and 2) the adaptive technique named Kullback-Leiber distance.
ISSN:1083-4435
1941-014X
DOI:10.1109/TMECH.2016.2531629