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

Authors:
Nicola Bellotto;Huosheng Hu
Affiliations:
School of Computer Science, University of Lincoln, Lincoln, UK LN6 7TS;School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK CO4 3SQ
Venue:
Autonomous Robots
Year:
2010

Citing 6
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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 neighborhood. 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.