F. Schönherr, J. Hertzberg, and W. Burgard
Probabilistic Mapping of Unexpected
Objects by a Mobile Robot
Proc. of the IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS)
Abstract
Map learning methods are generally designed to learn from scratch and start
with zero knowledge about the state of the world. In this paper, we present
a technique for extending a given metric map of the environment by objects
of a known type, where localization and perception of the robot is allowed
to be uncertain. The advantage of our approach is that it allows the robot
to estimate its own position in the given outline of the environment and
thus to estimate the position of the objects not contained in the map.
The method relies on partially observable Markov decision processes as
well as on the Baum-Welch algorithm. It has been implemented and evaluated
in several simulation experiments and also in a real-world sewage pipe
network. The experimental results demonstrate that our approach can efficiently
and accurately estimate the position of unexpected objects. Because of
the probabilistic nature of the underlying techniques, our method can deal
with noisy sensors as well as with large odometry errors which generally
occur when deploying a robot in a sewerage pipe system.
Download
Full paper [.ps.gz](number
bytes)
Bibtex
@InProceedings{Sch99Pro,
author = {Sch{\"o}nherr, F. and Hertzberg,
J. and Burgard, W.},
title = "Probabilistic Mapping of
Unexpected Objects by a Mobile Robot",
booktitle = IROS,
year = 1998
}