Sciweavers

Share
AROBOTS
2010

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

8 years 6 months ago
Computationally efficient solutions for tracking people with a mobile robot: an experimental evaluation of Bayesian filters
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 tha...
Nicola Bellotto, Huosheng Hu
Added 01 Feb 2011
Updated 01 Feb 2011
Type Journal
Year 2010
Where AROBOTS
Authors Nicola Bellotto, Huosheng Hu
Comments (0)
books