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A Combined Monte-Carlo Localization and Tracking Algorithm for RoboCup

9 years 8 months ago
A Combined Monte-Carlo Localization and Tracking Algorithm for RoboCup
— Self-localization is a major research task in mobile robotics for several years. Efficient self-localization methods have been developed, among which probabilistic Monte-Carlo localization (MCL) is one of the most popular. It enables robots to localize themselves in real-time and to recover from localization errors. However, even those versions of MCL using an adaptive number of samples need at least a minimum in the order of 100 samples to compute an acceptable position estimation. This paper presents a novel approach to MCL based on images from an omnidirectional camera system. The approach uses an adaptive number of samples that drops down to a single sample if the pose estimation is sufficiently accurate. We show that the method enters this efficient tracking mode after a few cycles and remains there using only a single sample for more than 90% of the cycles. Nevertheless, it is still able to cope with the kidnapped robot problem.
Patrick Heinemann, Jürgen Haase, Andreas Zell
Added 12 Jun 2010
Updated 12 Jun 2010
Type Conference
Year 2006
Where IROS
Authors Patrick Heinemann, Jürgen Haase, Andreas Zell
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