Slawomir Grzonka, Christian Plagemann, Giorgio Grisetti, Wolfram Burgard
Look-ahead Proposals for Robust Grid-based SLAM
Abstract:
Simultaneous Localization and Mapping (SLAM) is one of the classical
problems in mobile robotics. The task is to build a map of the
environment using on-board sensors while at the same time localizing
the robot relative to this map. Rao-Blackwellized particle filters
have emerged as a powerful technique for solving the SLAM problem in a
wide variety of environments. It is a well-known fact for
sampling-based approaches that the choice of the proposal distribution
greatly influences the robustness and efficiency achievable by the
algorithm. In this paper, we present a significantly improved
proposal distribution for grid-based SLAM, which utilizes whole
sequences of sensor measurements rather than only the most recent one.
We have implemented our system on a real robot and evaluated its
performance on standard data sets as well as in hard outdoor settings
with few and ambiguous features. Our approach improves the
localization accuracy and the map quality. At the same time, it
substantially reduces the risk of mapping failures.
Bibtex:
@InProceedings{grzonka07fsr,
author = {Grzonka, S. and Plagemann, C. and Grisetti, G. and Burgard, W.},
title = {Look-ahead Proposals for Robust Grid-based SLAM},
booktitle = {Proc. of the International Conference on Field and Service Robotics (FSR)},
year = {2007},
month = {July},
address = {Chamonix, France},
}
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