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Humanoid Robotics Laboratory

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Publication Search Results

Now showing 1 - 2 of 2
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    Detecting Partially Occluded Objects via Segmentation and Validation
    (Georgia Institute of Technology, 2012) Levihn, Martin ; Dutton, Matthew ; Trevor, Alexander J. B. ; Stilman, Mike
    This paper presents a novel algorithm: Verfied Partial Object Detector (VPOD) for accurate detection of partially occluded objects such as furniture in 3D point clouds. VPOD is implemented and validated on real sensor data obtained by our robot. It extends Viewpoint Feature Histograms (VFH) which classify unoccluded objects to also classifying partially occluded objects such as furniture that might be seen in typical office environments. To achieve this result, VPOD employs two strategies. First, object models are segmented and the object database is extended to include partial models. Second, once a matching partial object is detected, the full object model is aligned back into the scene and verified for consistency with the point cloud data. Overall, our approach increases the number of objects found and substantially reduces false positives due to the verification process.
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    Efficient Opening Detection
    (Georgia Institute of Technology, 2011) Levihn, Martin ; Stilman, Mike
    We present an efficient and powerful algorithm for detecting openings. Openings indicate the existence of a new path for the robot. The reliable detection of new openings is especially relevant to the domain of Navigation Among Movable Obstacles in known [7] as well as unknown [2] environments. Tremendous speed-ups for algorithms in these domains can be achieved by limiting the considerations of obstacle manipulations to cases where manipulations create new openings. The presented algorithm can detect openings for obstacles of arbitrary shapes being displaced in arbitrary directions in changing environments. To the knowledge of the authors, this is the first algorithm to achieve efficient opening detection for arbitrary shaped obstacles.