Registration of colored 3D point clouds with a Kernel-based extension to the normal distributions transform

We present a new algorithm for scan registration of colored 3D point data which is an extension to the normal distributions transform (NDT). The probabilistic approach of NDT is extended to a color-aware registration algorithm by modeling the point distributions as Gaussian mixture-models in color space. We discuss different point cloud registration techniques, as well as alternative variants of the proposed algorithm. Results showing improved robustness of the proposed method using real-world data acquired with a mobile robot and a time-of-flight camera are presented.

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