  Pavement condition is crucial for civil infrastructure maintenance. This task
usually requires efficient road damage localization, which can be accomplished
by the visual odometry system embedded in unmanned aerial vehicles (UAVs).
However, the state-of-the-art visual odometry and mapping methods suffer from
large drift under the degeneration of the scene structure. To alleviate this
issue, we integrate normal constraints into the visual odometry process, which
greatly helps to avoid large drift. By parameterizing the normal vector on the
tangential plane, the normal factors are coupled with traditional reprojection
factors in the pose optimization procedure. The experimental results
demonstrate the effectiveness of the proposed system. The overall absolute
trajectory error is improved by approximately 20%, which indicates that the
estimated trajectory is much more accurate than that obtained using other
state-of-the-art methods.
