Estimating Initial Guess of Localization by Line Matching in Lidar Intensity Maps

While driving in typical traffic scenes with drastic drift or sudden jump of GPS positions, the localization methods based on wrong initial positions could not select the properly overlapping data from the pre-built map to match with current data, rendering the localizations as not feasible. In this paper, we first propose to estimate an initial position by matching in the infrared reflectivity maps. The maps consists of a highly precise prior map built with offline SLAM technique and a smooth current map built with the integral over velocities. Considering the attributes of the low-texture maps, we adopt the stable, rich line segments to match. A affinity graph to measure the pairwise consistency of the candidate line matches is constructed using the local appearance, pairwise geometric attribute and is efficiently solved with a spectral technique. The initial global position is obtained by converting the structure between current position and matched lines. Experiment on the campus with GPS error of dozens of meters shows that our algorithm can provide an robust initial value with meter-level accuracy.