LIDAR-inertial integration for UAV localization and mapping in complex environments

This paper presents customized techniques for autonomous localization and mapping of micro Unmanned Aerial Vehicles flying in complex environments, e.g. unexplored, full of obstacles, GPS challenging or denied. The proposed algorithms are aimed at 2D environments and are based on the integration of 3D data, i.e. point clouds acquired by means of a laser scanner (LIDAR), and inertial data given by a low cost Inertial Measurement Unit (IMU). Specifically, localization is performed by exploiting a scan matching approach based on a customized version of the Iterative Closest Point algorithm, while mapping is done by extracting robust line features from LIDAR measurements. A peculiarity of the line detection method is the use of the Principal Component Analysis which allows computational time saving with respect to traditional least squares techniques for line fitting. Performance of the proposed approaches is evaluated on real data acquired in indoor environments by means of an experimental setup including an UTM-30LX-EW 2D LIDAR, a Pixhawk IMU, and a Nitrogen board.