  In this paper we propose a novel semantic localization algorithm that
exploits multiple sensors and has precision on the order of a few centimeters.
Our approach does not require detailed knowledge about the appearance of the
world, and our maps require orders of magnitude less storage than maps utilized
by traditional geometry- and LiDAR intensity-based localizers. This is
important as self-driving cars need to operate in large environments. Towards
this goal, we formulate the problem in a Bayesian filtering framework, and
exploit lanes, traffic signs, as well as vehicle dynamics to localize robustly
with respect to a sparse semantic map. We validate the effectiveness of our
method on a new highway dataset consisting of 312km of roads. Our experiments
show that the proposed approach is able to achieve 0.05m lateral accuracy and
1.12m longitudinal accuracy on average while taking up only 0.3% of the storage
required by previous LiDAR intensity-based approaches.
