  High-definition map with accurate lane-level information is crucial for
autonomous driving, but the creation of these maps is a resource-intensive
process. To this end, we present a cost-effective solution to create lane-level
roadmaps using only the global navigation satellite system (GNSS) and a camera
on customer vehicles. Our proposed solution utilizes a prior
standard-definition (SD) map, GNSS measurements, visual odometry, and lane
marking edge detection points, to simultaneously estimate the vehicle's 6D
pose, its position within a SD map, and also the 3D geometry of traffic lines.
This is achieved using a Bayesian simultaneous localization and multi-object
tracking filter, where the estimation of traffic lines is formulated as a
multiple extended object tracking problem, solved using a trajectory Poisson
multi-Bernoulli mixture (TPMBM) filter. In TPMBM filtering, traffic lines are
modeled using B-spline trajectories, and each trajectory is parameterized by a
sequence of control points. The proposed solution has been evaluated using
experimental data collected by a test vehicle driving on highway. Preliminary
results show that the traffic line estimates, overlaid on the satellite image,
generally align with the lane markings up to some lateral offsets.
