This paper describes an approach to solve the
Simultaneous Localization and Mapping (SLAM) problem for
autonomous mobile robots using visual landmarks. Our map is
represented by a set of three dimensional landmarks referred to a
global reference frame. We use significant points extracted from
stereo images as natural landmarks, in particular we employ
SIFT features found in the environment. Each landmark contains
a visual descriptor that partially differentiates it from others. Our
method is based on a Rao-Blackwellized particle filter, thus the
problem is decomposed into two parts: one estimation over robot
paths, and N independent estimations over landmark positions,
each conditioned on the path estimate. We actively track visual
landmarks at a local neighbourhood and select only those that
are more stable. When a visual feature has been observed from
a significant number of frames it is then integrated in the filter.
By this procedure, the total number of landmarks in the map
is reduced, compared to prior approaches. Due to the tracking
of each landmark, we obtain different examples that represent
the same natural landmark. We use this fact to improve data
association. Finally, efficient resampling techniques have been
applied, which reduces the number of particles needed and avoids
the particle depletion problem.
Paper: [pdf: 613k]
Bibtex
@InProceedings{gil2006iros,
TITLE = {Improving Data Association in Rao-Blackwellized visual SLAM},
AUTHOR = {Arturo Gil and Oscar Reinoso and Wolfram Burgard and Cyrill Stachniss and Oscar Martinez Mozos},
BOOKTITLE = {Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems},
PAGES = {2076--2081},
ADDRESS = {Beijing, China},
YEAR = {2006},
URL = {http://www.informatik.uni-freiburg.de/~omartine/publications/gil2006iros.pdf}
}