  This paper presents DLL, a direct map-based localization technique using 3D
LIDAR for its application to aerial robots. DLL implements a point cloud to map
registration based on non-linear optimization of the distance of the points and
the map, thus not requiring features, neither point correspondences. Given an
initial pose, the method is able to track the pose of the robot by refining the
predicted pose from odometry. Through benchmarks using real datasets and
simulations, we show how the method performs much better than Monte-Carlo
localization methods and achieves comparable precision to other
optimization-based approaches but running one order of magnitude faster. The
method is also robust under odometric errors. The approach has been implemented
under the Robot Operating System (ROS), and it is publicly available.
