View source for Learning pose estimation for UAV autonomous navigation and landing using visual-inertial sensor data

{{Paper
|Title=Learning pose estimation for UAV autonomous navigation and landing using visual-inertial sensor data
|Authors=Francesca Baldini, Animashree Anandkumar, Richard M. Murray
|Source=2020 American Control Conference (ACC)
|Abstract=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.
|URL=https://authors.library.caltech.edu/100568/1/1912.04527.pdf
|Type=Conference paper
|ID=2019i
|Tag=BAM20-acc
|Funding=NSF VeHICaL
}}