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Point feature extraction on 3D range scans taking into account object boundaries

Point feature extraction on 3D range scans taking into account object boundaries,10.1109/ICRA.2011.5980187,Bastian Steder,Radu Bogdan Rusu,Kurt Konoli

Point feature extraction on 3D range scans taking into account object boundaries   (Citations: 2)
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In this paper we address the topic of feature ex- traction in D point cloud data for object recognition and pose identification. We present a novel interest keypoint extraction method that operates on range images generated from arbitrary 3D point clouds, which explicitly considers the borders of the objects identified by transitions from foreground to back- ground. We furthermore present a feature descriptor that takes the same information into account. We have implemented our approach and present rigorous experiments in which we analyze the individual components with respect to their repeatability and matching capabilities and evaluate the usefulness for point feature based object detection methods. I. INTRODUCTION In object recognition or mapping applications, the ability to find similar parts in different sets of sensor readings is a highly relevant problem. A popular method is to estimate features that best describe a chunk of data in a compressed representation and that can be used to efficiently perform comparisons between different data regions. In 2D or 3D perception, such features are usually local around a point in the sense that for a given point in the scene its vicinity is used to determine the corresponding feature. The entire task is typically subdivided into two subtasks, namely the identification of appropriate points, often referred to as interest points or key points, and the way in which the information in the vicinity of that point is encoded in a descriptor or description vector. Important advantages of interest points are that they substantially reduce the search space and computation time required for finding correspondences between two scenes and that they furthermore focus the computation on areas that are more likely relevant for the matching process. There has been surprisingly little research for interest point extraction in raw 3D data in the past, compared to vision, where this is a well researched area. Most papers about 3D features target only the descriptor. In this paper we focus on single range scans, as obtained with 3D laser range finders or stereo cameras, where the data is incomplete and dependent on a viewpoint. We chose range images as the way to represent the data since they reflect this situation and enable us to borrow ideas from the vision sector. We present the normal aligned radial feature (NARF), a novel interest point extraction method together with a feature descriptor for points in 3D range data. The interest point
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