Computationally efficient solutions for tracking people with a mobile robot: an experimental evaluation of Bayesian filters

Bellotto, Nicola and Hu, Huosheng (2010) Computationally efficient solutions for tracking people with a mobile robot: an experimental evaluation of Bayesian filters. Autonomous Robots, 28 (4). pp. 425-438. ISSN 0929-5593

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Official URL: http://dx.doi.org/10.1007/s10514-009-9167-2

Abstract

Modern service robots will soon become an essential part of modern society. As they have to move and act in human environments, it is essential for them to be provided with a fast and reliable tracking system that localizes people in the neighbourhood. It is therefore important to select the most appropriate filter to estimate the position of these persons.
This paper presents three efficient implementations of multisensor-human tracking based on different Bayesian estimators: Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Sampling Importance Resampling (SIR) particle filter. The system implemented on a mobile robot is explained, introducing the methods used to detect and estimate the position of multiple people. Then, the solutions based on the three filters are discussed in detail. Several real experiments are conducted to evaluate their performance, which is compared in terms of accuracy, robustness and execution time of the estimation. The results show that a solution based on the UKF can perform as good as particle filters and can be often a better choice when computational efficiency is a key issue.

Item Type:Article
Keywords:People Tracking, Mobile Robot, Kalman Filter, Particle Filter, Multisensor Fusion
Subjects:H Engineering > H670 Robotics and Cybernetics
Divisions:College of Science > School of Computer Science
ID Code:2286
Deposited By: Nicola Bellotto
Deposited On:09 Apr 2010 14:34
Last Modified:18 Nov 2013 16:00

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