Fast Localization of Autonomous Vehicles using Discriminative Metric Learning

In this paper, we report a novel algorithm for localization of autonomous vehicles in an urban environment using orthographic ground reflectivity map created with a three-dimensional (3D) laser scanner. It should be noted that the road paint (lane markings, zebra crossing, traffic signs etc.) constitute the distinctive features in the surface reflectivity map which are generally sparse as compared to the non-interesting asphalt and the off-road portion of the map. Therefore, we propose to project the reflectivity map to a lower dimensional space, that captures the useful features of the map, and then use these projected feature maps for localization. We use discriminative metric learning technique to obtain this lower dimensional space of feature maps. Experimental evaluation of the proposed method on real data shows that it is better than the standard image matching techniques in terms of accuracy. Moreover, the proposed method is computationally fast and can be executed at real-time (10 Hz) on a standard CPU.