  Higher level functionality in autonomous driving depends strongly on a
precise motion estimate of the vehicle. Powerful algorithms have been
developed. However, their great majority focuses on either binocular imagery or
pure LIDAR measurements. The promising combination of camera and LIDAR for
visual localization has mostly been unattended. In this work we fill this gap,
by proposing a depth extraction algorithm from LIDAR measurements for camera
feature tracks and estimating motion by robustified keyframe based Bundle
Adjustment. Semantic labeling is used for outlier rejection and weighting of
vegetation landmarks. The capability of this sensor combination is demonstrated
on the competitive KITTI dataset, achieving a placement among the top 15. The
code is released to the community.
