  This paper presents a set of novel scan-matching techniques for vehicle pose
estimation using automotive radar measurements. The proposed approach modifies
the Normal Distributions Transform (NDT) -- a state-of-the-art scan-matching
SLAM technique, widely used in lidar-based localization -- to account for
particular aspects of radar environment perception. First, the polar NDT (PNDT)
is introduced by solving the NDT problem in the polar coordinate system,
natural for radar measurements. A better agreement between the measurement
uncertainties and their representation in the scan-matching algorithm is
achieved. Second, the extension of PNDT to take into account the Doppler
measurements -- Doppler polar NDT (DPNDT) -- is proposed. Third, the SNR of
detected targets is added to the optimization procedure to minimize the impact
of RCS fluctuation. The improvement over the conventional NDT is demonstrated
in numerical simulations and real data processing, showing the ability to
decrease the localization error by a factor of 3 to 5, depending on the
scenario, with a negligible increase in computational complexity. Finally, a
DPNDT extension with the capability to compensate angular bias in array
beam-forming is presented. Simulation results and real data processing show the
possibility to correct it with the accuracy of $0.1^\circ$ almost in real-time.
