DLL: Direct LIDAR Localization. A map-based localization approach for aerial robots
This work addresses localization for aerial robots, offering a faster alternative to existing methods, though it is incremental as it builds on optimization-based approaches.
The paper tackles the problem of fast and accurate localization for aerial robots by introducing DLL, a direct LIDAR-based method that optimizes point-to-map distances without features or correspondences. It achieves comparable precision to other optimization-based approaches but runs one order of magnitude faster, as demonstrated in benchmarks with real datasets and simulations.
This paper presents DLL, a fast 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.