<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p></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.

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

Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

In this paper, a unified and probabilistic method is proposed for simultaneously localization of a mobile service robot and states estimation of surrounding...

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

Published Online: Dec 01, 2012

Page range: 1081 - 1096

Received: Jul 21, 2012

Accepted: Oct 12, 2012

DOI: https://doi.org/10.21307/ijssis-2017-524

© 2012 Kun Qian et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.