Laser-only road-vehicle localization with dual 2D push-broom LIDARS and 3D priors

We demonstrate the viability of using 2D LIDAR data as the sole means for accurate, robust, long-term road-vehicle localization within a prior map in a complex, dynamic real-world setting. We utilize a dual-LIDAR system - one oriented horizontally, in order to infer vehicle linear and rotational velocity, and one declined to capture a dense view of the surrounds - that allows us to estimate both velocity and position within a prior map. We show how probabilistically modelling the noisy local velocity estimates from the horizontal laser feed, fusing these estimates with data from the declined LIDAR to form a dense 3D swathe and matching this swathe statistically within a map will allow for robust, long-term position estimation. We accommodate estimation errors induced by passing vehicles, pedestrians, ground-strike etc., by learning a positional-dependent sensor model - that is, a sensor-model that varies spatially - and show that learning such a model for LIDAR data allows us to deal gracefully with the complexities of realworld data. We validate the concept over more than 9 kilometres of driven distance in and around the town of Woodstock, Oxfordshire.