Probabilistic Joint State Estimation of Robot and Non-static Objects for Mobile Manipulation

Kun Qian; Xudong Ma; Xian Zhong Dai; Fang Fang; Bo Zhou

doi:10.21307/ijssis-2017-524

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Probabilistic Joint State Estimation of Robot and Non-static Objects for Mobile Manipulation

International Journal on Smart Sensing and Intelligent Systems

Volume 5 (2012): Issue 4 (January 2012)

By: Kun Qian, Xudong Ma, Xian Zhong Dai, Fang Fang and Bo Zhou

Open Access

|Dec 2012

Abstract

In this paper, a unified and probabilistic method is proposed for simultaneously localization of a mobile service robot and states estimation of surrounding objects and co-existing people. This method allows intelligent robots to navigate reliably in dynamic environments and provide home-care services based on joint localization results. The algorithm makes use of probabilistic model to represent non-static people and objects states. Moreover, Rao-Blackwellized particle filters (RBPFs) are utilized for efficient joint estimation and laser sensing based smooth observation model is also introduced. The resulting algorithm works in real-time and estimates the position of people and state of doors with sufficient precision. Our approach has been tested in typical indoor environment with people, doors and other non-static objects. Experimental results demonstrate the favorable performance of the position estimation accuracy as well as the capability to deal with the uncertainty of mobile sensing.

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Articles in this issue

DOI: https://doi.org/10.21307/ijssis-2017-524 | Journal eISSN: 1178-5608

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Language: English

Page range: 1081 - 1096

Submitted on: Jul 21, 2012

Accepted on: Oct 12, 2012

Published on: Dec 1, 2012

Published by: International Journal on Smart Sensing and Intelligent Systems

In partnership with: Paradigm Publishing Services

Publication frequency: 1 issue per year

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© 2012 Kun Qian, Xudong Ma, Xian Zhong Dai, Fang Fang, Bo Zhou, published by International Journal on Smart Sensing and Intelligent Systems
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 5 (2012): Issue 4 (January 2012)