M. Veeck, W. Burgard
Learning Polyline Maps from Range
Scan Data Acquired with Mobile Robots
In Proc. of the IEEE Conference on Intelligent Robots and Systems
(IROS), 2004.
Abstract:
Geometric representations of the environment play an important role in mobile
robotics as they support various tasks such as motion control and accurate
localization. Popular approaches to represent the geometric features of an
environment are occupancy grids or line models. Whereas occupancy grids
require a huge amount of memory and therefore do not scale well with the size
of the environment, line models are unable to correctly represent corners or
connections between objects. In this paper we present an algorithm that
learns sets of polylines from laser range scans. Starting with an initial set
of polylines generated from the range scans it iteratively optimizes these
polylines using the Bayesian Information Criterion. During the optimization
process our algorithm utilizes information about the angles between line
segments extracted from the original range scans. We present experiments
illustrating that our algorithm is able to learn accurate and highly compact
polyline maps from laser range data obtained with mobile robots.
Bibtex:
@InProceedings{veeck04iros,
author = {Veeck, M. and Burgard, W.},
title = {Learning Polyline Maps from Range Scan Data Acquired with Mobile Robots},
booktitle = {Proc.~of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year = {2004}
}
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