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

Abstract-- In this work, we propose a robust network-in-the-loop control system that allows an Unmanned-Aerial-Vehicles to navigate and land autonomously on a desired target. To estimate the global pose of the aerial vehicle, we develop a deep neural network architecture for visual-inertial odometry, which provides a robust alternative to traditional techniques for autonomous navigation of Unmanned-Aerial-Vehicles. We first provide experimental results on 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% against the baseline. Second, we use Airsim, a simulator available as a plugin for Unreal Engine, to create new datasets of photorealistic images and inertial measurement to train and test our model. We finally integrate the proposed architecture for global localization with the Airsim closed-loop control system, and we provide simulation results for the autonomous landing of the aerial vehicle. I. INTRODUCTION Unmanned-Aerial-Vehicles (UAVs) can provide significant support for many applications, such as rescue operations, environmental monitoring, package delivery, and surveillance. To guarantee a high safety level in the UAV operation, it is crucial to have continuous monitoring of the state of the vehicle. Currently, the most standard techniques deployed for pose estimation are Visual-Inertial Odometry (VIO) [1, 2] and Simultaneous Localization and Mapping (SLAM) [3-5].