Airborne vision-based attitude estimation and localisation

Vision plays an integral part in a pilot's ability to navigate and control an aircraft. Therefore Visual Flight Rules have been developed around the pilot's ability to see the environment outside of the cockpit in order to control the attitude of the aircraft, to navigate and to avoid obstacles. The automation of these processes using a vision system could greatly increase the reliability and autonomy of unmanned aircraft and flight automation systems. This thesis investigates the development and implementation of a robust vision system which fuses inertial information with visual information in a probabilistic framework with the aim of aircraft navigation. The horizon appearance is a strong visual indicator of the attitude of the aircraft. This leads to the first research area of this thesis, visual horizon attitude determination. An image processing method was developed to provide high performance horizon detection and extraction from camera imagery. A number of horizon models were developed to link the detected horizon to the attitude of the aircraft with varying degrees of accuracy. The second area investigated in this thesis was visual localisation of the aircraft. A terrain-aided horizon model was developed to estimate the position, altitude as well as attitude of the aircraft. This gives rough positions estimates with highly accurate attitude information. The visual localisation accuracy was improved by incorporating ground feature-based map-aided navigation. Road intersections were detected using a developed image processing algorithm and then they were matched to a database to provide positional information. The developed vision system show comparable performance to other non-vision-based systems while removing the dependence on external systems for navigation. The vision system and techniques developed in this thesis helps to increase the autonomy of unmanned aircraft and flight automation systems for manned flight

Learning Pose Estimation for UAV Autonomous Navigation and Landing Using Visual-Inertial Sensor Data

In this work, we propose a robust network-in-the-loop control system for autonomous navigation and landing of an Unmanned-Aerial-Vehicle (UAV). To estimate the UAV’s absolute pose, we develop a deep neural network (DNN) architecture for visual-inertial odometry, which provides a robust alternative to traditional methods. We first evaluate the accuracy of the estimation by comparing the prediction of our model to traditional visual-inertial approaches on the publicly available EuRoC MAV dataset. The results indicate a clear improvement in the accuracy of the pose estimation up to 25% over the baseline. Finally, we integrate the data-driven estimator in the closed-loop flight control system of Airsim, a simulator available as a plugin for Unreal Engine, and we provide simulation results for autonomous navigation and landing

Cooperative monocular-based SLAM for multi-UAV systems in GPS-denied environments

This work presents a cooperative monocular-based SLAM approach for multi-UAV systems that can operate in GPS-denied environments. The main contribution of the work is to show that, using visual information obtained from monocular cameras mounted onboard aerial vehicles flying in formation, the observability properties of the whole system are improved. This fact is especially notorious when compared with other related visual SLAM configurations. In order to improve the observability properties, some measurements of the relative distance between the UAVs are included in the system. These relative distances are also obtained from visual information. The proposed approach is theoretically validated by means of a nonlinear observability analysis. Furthermore, an extensive set of computer simulations is presented in order to validate the proposed approach. The numerical simulation results show that the proposed system is able to provide a good position and orientation estimation of the aerial vehicles flying in formation.Peer ReviewedPostprint (published version

Integration of Absolute Orientation Measurements in the KinectFusion Reconstruction pipeline

In this paper, we show how absolute orientation measurements provided by low-cost but high-fidelity IMU sensors can be integrated into the KinectFusion pipeline. We show that integration improves both runtime, robustness and quality of the 3D reconstruction. In particular, we use this orientation data to seed and regularize the ICP registration technique. We also present a technique to filter the pairs of 3D matched points based on the distribution of their distances. This filter is implemented efficiently on the GPU. Estimating the distribution of the distances helps control the number of iterations necessary for the convergence of the ICP algorithm. Finally, we show experimental results that highlight improvements in robustness, a speed-up of almost 12%, and a gain in tracking quality of 53% for the ATE metric on the Freiburg benchmark.Comment: CVPR Workshop on Visual Odometry and Computer Vision Applications Based on Location Clues 201