Lidar-based scale recovery dense SLAM for UAV navigation

Imagine of having an autonomous agent (drone, robot, car, ..) that wants to navigate inside an unknown environment. The first question that it needs to answer for accomplish such task is: where Am I? Where are the objects that are surrounding me? The SLAM algorithm can answer to both questions simultaneously, in an on-line manner. This thesis focus on the implementation of a monocular SLAM algorithm on the UAV framework, where the classical obtained sparsity map is densified by means of a Convolutional Neural Network, properly scaled through 2D lidar measurements.