Michael Kusenbach

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This paper proposes an object recognition approach intended for extracting, analyzing and clustering of features from RGB image views from given objects. Extracted features are matched with features in learned object models and clustered in Hough-space to find a consistent object pose. Hypotheses for valid poses are verified by computing a homography from(More)
We present a new mapping and navigation system based on human-recognizable landmarks with highly compact representations. Road segments, intersections and salient structures such as houses and trees are detected using vision and LiDAR data. The landmarks are entered in a sparse metric-topological map that is used for navigation. In contrast to traditional(More)
In this paper, we introduce a new geometric 3D feature combined with a clustering approach. Besides 3D data provided by a LiDAR point cloud, reflectivity information is used to further enhance the descriptivity of the feature. The proposed feature can be extracted and compared in real-time. Similar parts of an object, such as features belonging to an(More)
A robust and reliable person detection is crucial for many applications. In the domain of service robots that we focus on, knowing the location of a person is an essential requirement for any meaningful human-robot interaction. In this work we present a people detection algorithm exploiting RGB-D data from Kinect-like cameras. Two features are obtained from(More)
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