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In this paper, we present a novel algorithm for point cloud registration forrange sensors capable of measuring per-return instantaneous radial velocity:Doppler ICP. Existing variants of ICP that solely rely on geometry or otherfeatures generally fail to estimate the motion of the sensor correctly inscenarios that have non-distinctive features and/or repetitive geometricstructures such as hallways, tunnels, highways, and bridges. We propose a newDoppler velocity objective function that exploits the compatibility of eachpoint's Doppler measurement and the sensor's current motion estimate. Wejointly optimize the Doppler velocity objective function and the geometricobjective function which sufficiently constrains the point cloud alignmentproblem even in feature-denied environments. Furthermore, the correspondencematches used for the alignment are improved by pruning away the points fromdynamic targets which generally degrade the ICP solution. We evaluate ourmethod on data collected from real sensors and from simulation. Our resultsshow that with the added Doppler velocity residual terms, our method achieves asignificant improvement in registration accuracy along with faster convergence,on average, when compared to classical point-to-plane ICP that solely relies ongeometric residuals.

DICP: Doppler Iterative Closest Point Algorithm