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Metric Localization with Scale-Invariant Visual Features using a Single Perspective Camera

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. 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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  The animated gif (2 MB) shows the evolution of the particle clouds during an localization experiment. The blue dot corresponds to the true pose of the robot and the green dot indicates the pose resulting from odometry information.
  This video (wmv, 19 MB) shows the humanoid robot Max collecting data in an office environment. Since the robot was designed for playing soccer, its camera looks downwards. Thus, in the experiment shown here, Max has to bend backwards in order to observe the features used for localization in the environment.
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