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This paper reports on a novel nonparametric rigid point cloud registrationframework that jointly integrates geometric and semantic measurements such ascolor or semantic labels into the alignment process and does not requireexplicit data association. The point clouds are represented as nonparametricfunctions in a reproducible kernel Hilbert space. The alignment problem isformulated as maximizing the inner product between two functions, essentially asum of weighted kernels, each of which exploits the local geometric andsemantic features. As a result of the continuous models, analytical gradientscan be computed, and a local solution can be obtained by optimization over therigid body transformation group. Besides, we present a new point cloudalignment metric that is intrinsic to the proposed framework and takes intoaccount geometric and semantic information. The evaluations using publiclyavailable stereo and RGB-D datasets show that the proposed method outperformsstate-of-the-art outdoor and indoor frame-to-frame registration methods. Anopen-source GPU implementation is also provided.

A New Framework for Registration of Semantic Point Clouds from Stereo and RGB-D Cameras