Metric Localization with Scale-Invariant Visual Features using a Single
Camera
- Author: Maren Bennewitz, Cyrill Stachniss, Wolfram Burgard, and
Sven Behnke
- In Proceedings of European Robotics Symposium (EUROS-06),
Palermo,
Italy, vol. 22 of Springer STAR series, pp. 143-157, March 2006.
- Abstract:
The Scale Invariant Feature Transform (SIFT) has become a popular
feature extractor for vision-based applications. It has been
successfully applied to metric localization and mapping using stereo
vision and omnivision.
In this paper, we present an approach to Monte-Carlo localization using
SIFT features for mobile robots equipped with a single perspective
camera. First, we acquire a 2D grid map of the environment that
contains the visual features. To come up with a
compact environmental model, we appropriately down-sample the number of
features in the final map. During localization, we cluster close-by
particles and estimate for each cluster the set of potentially visible
features in the map using ray-casting. These relevant map features are
then compared to the features extracted from the current image. The
observation model used to evaluate the individual particles considers
the difference between the measured and the expected angle of similar
features.
In real-world experiments, we demonstrate that our technique is able to
accurately track the position of a mobile robot. Moreover, we present
experiments illustrating that a robot equipped with a different type of
camera can use the same map of SIFT features for localization.
- Paper: EUROS06.pdf
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