Vision SLAM in the Measurement Subspace
John Folkesson, Patric Jensfelt and Henrik I. Christensen
Abstract:
n this paper we describe an approach to feature representation for
simultaneous localization and mapping, SLAM.
It is a general representation for features that
addresses symmetries and constraints in the feature
coordinates. Furthermore, the representation allows for the features
to be added to the map with partial initialization. This is an
important property when using oriented vision features where angle information
can be used before their full pose is known. The number of the
dimensions for a feature can grow with time as more information is
acquired. At the same time as the special properties of each type of
feature are accounted for, the commonalities of all map features are
also exploited to allow SLAM algorithms to be interchanged as well as
choice of sensors and features. In other words the SLAM implementation
need not be changed at all when changing sensors and features and vice
versa. Experimental results both with vision and range data and
combinations thereof are presented.
BibTeX Entry:
@InProceedings{Folkesson05a,
author = {John Folkesson and Patric Jensfelt and Henrik Christensen},
title = {Vision {SLAM} in the Measurement Subspace},
booktitle = {Proc.~of the IEEE International Conference on Robotics and Automation (ICRA'05)},
year = 2005,
month = apr,
pages = {30--35}
}
Download: pdf (471k)