The Scale Invariant Feature Transform (SIFT) has become a popular feature
extractor for vision-ba... more 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.
The Scale Invariant Feature Transform (SIFT) has become a popular feature
extractor for vision-ba... more 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.
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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.
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.