Unmanned Aerial Vehicles (UAVs) present a high technological development rate nowadays. These vehicles can be used to perform dangerous and costly inspection procedures in structures with difficult access instead of human operators, but they still need a close monitoring. This thesis addresses the problem of using exclusively sensors on board UAVs to derive attitude determination tools and trajectory tracking strategies. Firstly, this work discusses the perception of the outside world by the vehicle and the formulation of a mathematical description containing information regarding its position and attitude relative to the structure. For this purpose, a geometry is set and the best fit to the data provided by a LiDAR sensor is selected, after a robust outlier filtering process. With this information, several methods for obtaining the attitude are proposed. These include a fast and comprehensive yaw estimator, based on continuity, and a closed-form solution for the Wahba's problem and a nonlinear filter for a full attitude estimation, both on the group of rotation matrices. A significant effort was devoted to the analysis of the entire procedure through simulation, from the creation of the LiDAR data to the application of the methods, for validation purposes. Both simulated and experimental results are provided for the performance evaluation of the perception algorithms. Building on these results, a nonlinear control strategy is designed with the objective of providing an accurate trajectory tracking control relative to the structure, with guaranteed asymptotic stability.