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A Bank of Unscented Kalman Filters for Multimodal Human Perception with Mobile Service Robots

Bellotto, N and Hu, H (2010) 'A Bank of Unscented Kalman Filters for Multimodal Human Perception with Mobile Service Robots.' International Journal of Social Robotics, 2 (1). pp. 121-136. ISSN 1875-4791


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A new generation of mobile service robots could be ready soon to operate in human environments if they can robustly estimate position and identity of surrounding people. Researchers in this field face a number of challenging problems, among which sensor uncertainties and real-time constraints. In this paper, we propose a novel and efficient solution for simultaneous tracking and recognition of people within the observation range of a mobile robot. Multisensor techniques for legs and face detection are fused in a robust probabilistic framework to height, clothes and face recognition algorithms. The system is based on an efficient bank of Unscented Kalman Filters that keeps a multi-hypothesis estimate of the person being tracked, including the case where the latter is unknown to the robot. Several experiments with real mobile robots are presented to validate the proposed approach. They show that our solutions can improve the robot?s perception and recognition of humans, providing a useful contribution for the future application of service robotics.

Item Type: Article
Uncontrolled Keywords: Robot perception � Human tracking and recognition � Bayesian estimation � Service robotics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 15 Jul 2015 18:10
Last Modified: 15 Jan 2022 01:17

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