UMR CNRS 7253

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en:pekin [2012/12/13 19:50] – created castilloen:pekin [2012/12/13 19:51] (current) – castillo
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 __Abstract__ __Abstract__
    
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 Mobile robots equipped with cheap single-beam LIDARs are well suited for acquiring 3D data of environment. But accurate calibration for data fusion is required. Existing calibration methods for multi-LIDAR require specific calibration target or known environment. We intend to develop a calibration method which requires no specifically placed calibration targets, no special environments, no or few manual operations on software. Our approach uses the multi-type geometry features in outdoor point cloud, such as point, line, plane or quadric. In order to increase its robustness and accuracy, we  defined a probabilistic distance measure to combine all types of geometry features into one registration process in same scale. Mobile robots equipped with cheap single-beam LIDARs are well suited for acquiring 3D data of environment. But accurate calibration for data fusion is required. Existing calibration methods for multi-LIDAR require specific calibration target or known environment. We intend to develop a calibration method which requires no specifically placed calibration targets, no special environments, no or few manual operations on software. Our approach uses the multi-type geometry features in outdoor point cloud, such as point, line, plane or quadric. In order to increase its robustness and accuracy, we  defined a probabilistic distance measure to combine all types of geometry features into one registration process in same scale.
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en/pekin.1355424641.txt.gz · Last modified: 2012/12/13 19:50 by castillo
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