Bastian Steder, Michael Ruhnke, Slawomir Grzonka, and Wolfram Burgard
Place Recognition in 3D Scans Using a Combination of Bag of Words
and Point Feature based Relative Pose Estimation
Place recognition, i.e., the ability to recognize previously
seen parts of the environment, is one of the fundamental
tasks in mobile robotics. The wide range of applications of
place recognition includes localization (determine the initial
pose), SLAM (detect loop closures), and change detection in
dynamic environments. In the past, only relatively little work
has been carried out to attack this problem using 3D range data
and the majority of approaches focuses on detecting similar
structures without estimating relative poses. In this paper, we
present an algorithm based on 3D range data that is able to
reliably detect previously seen parts of the environment and at
the same time calculates an accurate transformation between
the corresponding scan-pairs. Our system uses the estimated
transformation to evaluate a candidate and in this way to
more robustly reject false positives for place recognition. We
present an extensive set of experiments using publicly available
datasets in which we compare our system to other state-of-theart
approaches.
Bibtex:
@inproceedings{steder11iros,
author = {Steder, B. and Ruhnke, M. and Grzonka, S. and Burgard, W.},
booktitle = {Proc.~of the IEEE/RSJ International Conference on Intelligent Robots and
Systems (IROS)},
year = {2011},
title = {Place Recognition in {3D} Scans Using a Combination of Bag of Words and Point
Feature based Relative Pose Estimation}
}
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