This paper reports on a fast multiresolution scan matcher for vehicle localization in urban environments for self-driving cars. State-of-the-art approaches to vehicle localization rely on observing road surface reflectivity with a three-dimensional (3D) light detection and ranging (LIDAR) scanner to achieve centimeter-level accuracy. However, these approaches can often fail when faced with adverse weather conditions that obscure the view of the road paint (e.g., puddles and snowdrifts) or poor road surface texture. We propose a new scan matching algorithm that leverages Gaussian mixture maps to exploit the structure in the environment; these maps are a collection of Gaussian mixtures over the z-height distribution. We achieve real-time performance by developing a novel branch-and-bound, multiresolution approach that makes use of rasterized lookup tables of these Gaussian mixtures. Results are shown on two datasets that are 3.0 km: a standard trajectory and another under adverse weather conditions.
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