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Practical Vision-Based Monte Carlo Localization on a Legged Robot

Mohan Sridharan, Gregory Kuhlmann, and Peter Stone. Practical Vision-Based Monte Carlo Localization on a Legged Robot. In IEEE International Conference on Robotics and Automation, April 2005.
ICRA 2005

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Abstract

Mobile robot localization, the ability of a robot to determine its global position and orientation, continues to be a major research focus in robotics. In most past cases, such localization has been studied on wheeled robots with range-finding sensors such as sonar or lasers. In this paper, we consider the more challenging scenario of a legged robot localizing with a limited field-of-view camera as its primary sensory input. We begin with a baseline implementation adapted from the literature that provides a reasonable level of competence, but that exhibits some weaknesses in real-world tests. We propose a series of practical enhancements designed to improve the robot's sensory and actuator models that enable our robots to achieve a $50%$ improvement in localization accuracy over the baseline implementation. We go on to demonstrate how the accuracy improvement is even more dramatic when the robot is subjected to large unmodeled movements. These enhancements are each individually straightforward, but together they provide a roadmap for avoiding potential pitfalls when implementing Monte Carlo Localization on vision-based and/or legged robots.

BibTeX

@InProceedings(ICRA2005-localization,
       author="Mohan Sridharan and Gregory Kuhlmann and Peter Stone",
       title="Practical Vision-Based Monte Carlo Localization on a Legged Robot",
       booktitle="{IEEE} International Conference on Robotics and Automation",
       month="April",year="2005",
       abstract={
                 Mobile robot localization, the ability of a robot to
                 determine its global position and orientation,
                 continues to be a major research focus in robotics.
                 In most past cases, such localization has been
                 studied on wheeled robots with range-finding sensors
                 such as sonar or lasers.  In this paper, we consider
                 the more challenging scenario of a legged robot
                 localizing with a limited field-of-view camera as its
                 primary sensory input.  We begin with a
                 \emph{baseline} implementation adapted from the
                 literature that provides a reasonable level of
                 competence, but that exhibits some weaknesses in
                 real-world tests.  We propose a series of practical
                 enhancements designed to improve the robot's sensory
                 and actuator models that enable our robots to achieve
                 a $50\%$ improvement in localization accuracy over
                 the baseline implementation.  We go on to demonstrate
                 how the accuracy improvement is even more dramatic
                 when the robot is subjected to large unmodeled
                 movements. These enhancements are each individually
                 straightforward, but together they provide a roadmap
                 for avoiding potential pitfalls when implementing
                 Monte Carlo Localization on vision-based and/or
                 legged robots.
                 },
        wwwnote={<a href="http://www.icra2005.org/">ICRA 2005</a>},
)

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