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

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 experi...

• Type: Conference
• Year: 2006
• Where: EUROS
• Authors: Maren Bennewitz, Cyrill Stachniss, Wolfram Burgard, Sven Behnke
• Keywords: EUROS 2006, Invariant Feature Transform, Mobile Robot, Robotics, SIFT Features