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FAST MULTI-SCALE POINT CLOUD REGISTRATION WITH A HIERARCHICAL GAUSSIAN MIXTURE
FAST MULTI-SCALE POINT CLOUD REGISTRATION WITH A HIERARCHICAL GAUSSIAN MIXTURE
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机译:具有分层高斯混合的快速多尺度点云注册
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摘要
Point cloud registration sits at the core of many important and challenging 3D perception problems including autonomous navigation, object/scene recognition, and augmented reality (AR). A new registration algorithm is presented that achieves speed and accuracy by registering a point cloud to a representation of a reference point cloud. A target point cloud is registered to the reference point cloud by iterating through a number of cycles of an EM algorithm where, during an Expectation step, each point in the target point cloud is associated with a node of a hierarchical tree data structure and, during a Maximization step, an estimated transformation is determined based on the association of the points with corresponding nodes of the hierarchical tree data structure. The estimated transformation is determined by solving a minimization problem associated with a sum, over a number of mixture components, over terms related to a Mahalanobis distance.
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