  Accurate localization is of crucial importance for autonomous driving tasks.
Nowadays, we have seen a lot of sensor-rich vehicles (e.g. Robo-taxi) driving
on the street autonomously, which rely on high-accurate sensors (e.g. Lidar and
RTK GPS) and high-resolution map. However, low-cost production cars cannot
afford such high expenses on sensors and maps. How to reduce costs? How do
sensor-rich vehicles benefit low-cost cars? In this paper, we proposed a
light-weight localization solution, which relies on low-cost cameras and
compact visual semantic maps. The map is easily produced and updated by
sensor-rich vehicles in a crowd-sourced way. Specifically, the map consists of
several semantic elements, such as lane line, crosswalk, ground sign, and stop
line on the road surface. We introduce the whole framework of on-vehicle
mapping, on-cloud maintenance, and user-end localization. The map data is
collected and preprocessed on vehicles. Then, the crowd-sourced data is
uploaded to a cloud server. The mass data from multiple vehicles are merged on
the cloud so that the semantic map is updated in time. Finally, the semantic
map is compressed and distributed to production cars, which use this map for
localization. We validate the performance of the proposed map in real-world
experiments and compare it against other algorithms. The average size of the
semantic map is $36$ kb/km. We highlight that this framework is a reliable and
practical localization solution for autonomous driving.
