Clustering Multivariate Gaussians for Normal Distribution Transform Grids
نویسندگان
چکیده
3D maps including urban structures and terrain information facilitate urban searches for robots operating in various robot scenarios. However it suffers from large memory requirements and computation time to deal with point clouds collected by range sensors. It is problematic for operating rescue robots in real time. NDT (Normal Distribution Transform) grid is a suitable map model to deal with point clouds. However there is no segmentation method for NDT, since it consists of mean and covariance of multivariate Gaussians. Recent machine learning techniques give us a basic theory for applying to NDT segmentation. In this paper we design algorithm for NDT segmentation based on the theory in spite of difficulties. In experiments, point clouds collected in urban environments are tested to evaluate performance of the proposed segmentation algorithm.
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