A Markov Chain Monte Carlo Approach to Closing the Loop in SLAM
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Author(s)
Kaess, Michael
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Abstract
The problem of simultaneous localization
and mapping has received much attention over the last
years. Especially large scale environments, where the robot
trajectory loops back on itself, are a challenge. In this
paper we introduce a new solution to this problem of
closing the loop. Our algorithm is EM-based, but differs
from previous work. The key is a probability distribution
over partitions of feature tracks that is determined in
the E-step, based on the current estimate of the motion.
This virtual structure is then used in the M-step to
obtain a better estimate for the motion. We demonstrate
the success of our algorithm in experiments on real laser data.
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Date
2005-04
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