  Autonomous vehicles rely on precise high definition (HD) 3d maps for
navigation. This paper presents the mapping component of an end-to-end system
for crowdsourcing precise 3d maps with semantically meaningful landmarks such
as traffic signs (6 dof pose, shape and size) and traffic lanes (3d splines).
The system uses consumer grade parts, and in particular, relies on a single
front facing camera and a consumer grade GPS. Using real-time sign and lane
triangulation on-device in the vehicle, with offline sign/lane clustering
across multiple journeys and offline Bundle Adjustment across multiple journeys
in the backend, we construct maps with mean absolute accuracy at sign corners
of less than 20 cm from 25 journeys. To the best of our knowledge, this is the
first end-to-end HD mapping pipeline in global coordinates in the automotive
context using cost effective sensors.
