Particle Filtering-based tracking and localization on context-aware robotic system

Author

Kun Wang ; Liu, Xiaoping P.

Author_Institution

Dept. of Syst. & Comput. Eng., Carleton Univ., Ottawa, ON, Canada

fYear

2014

fDate

22-24 Aug. 2014

Firstpage

229

Lastpage

234

Abstract

This paper develops the algorithm of human tracking and localization implemented on a context-aware robotic platform. The tracking and localization algorithm is developed using the Particle Filtering (PF) method, enhanced by the adaptive multi-model techniques and the entropy-based active sensing. The proposed solution is then utilized for human tracking and localization on a mobile robot platform. The feasibility and effectiveness of the entropy and multi-model based particle filtering method is demonstrated in the experimental results.

Keywords

control engineering computing; entropy; mobile robots; object tracking; particle filtering (numerical methods); ubiquitous computing; PF method; adaptive multimodel techniques; context-aware robotic system; entropy-based active sensing; human tracking and localization; localization algorithm; mobile robot platform; multimodel based particle filtering method; particle filtering-based tracking and localization; Accuracy; Clocks; Entropy; Handheld computers; Robots; Tracking; Particle filtering; context-aware; entropy; location estimation; multi-model; object tracking;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Science & Education (ICCSE), 2014 9th International Conference on

Conference_Location

Vancouver, BC

Print_ISBN

978-1-4799-2949-8

Type

conf

DOI

10.1109/ICCSE.2014.6926459

Filename

6926459