3D occupancy grid mapping using statistical radar models

We have developed a numerically efficient occupancy grid mapping method in three dimensions for automotive radar, where we take into account the radar measurement signal-to-noise ratio. The mapping performance, i.e. to estimate length, height, and in-between spacing of parked cars, is demonstrated as we use acquired data from a radar prototype developed in collaboration with Qamcom Research and Technology
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. The radar has a unique antenna providing unambiguous azimuth and elevation for a wide field of view radar, covering -50° in both dimensions, making mapping in three dimensions feasible. Employing self-developed off-line radar signal processing on raw data, we extract SNR which is used together with a Swerling 1 model to compute the probability of detection for grid map update. Moreover, we present a novel very simplistic way of updating the grid as we use fast trilinear interpolation in the measurement domain, in which the grid spacing is uniform. Having mounted the radar in forward direction the EGO-vehicle drive parallel to four parked cars with different inter-spacing, and we manage to measure the distances within the error of the grid spacing, 0.2 m.