GPS emulation via visualinertial odometry for inspection drones This work tested the applicability of a navigation system based on cameras and inertial sensors for a quadrotor UAV, motivated by the desire to expand its operational envelope to regions with low GPS signal reception. A standalone navigation module composed of a stereo camera pair, an IMU, synchronization electronics and a SoC computational board was developed and provided with OKVIS, a recent opensource VIO (VisualInertial Odometry) algorithm. In order to quantitatively assess its performance, an indoors dataset was recorded in a controlled environment, with precise groundtruth from a motion capture system. The system was integrated with a quadrotor UAV, functioning as an additional GPS sensor from the perspective of the onboard autopilot. For this end, the VIO trajectory data was georeferenced using information from the onboard GPS receiver, as well as from the orientation estimator embedded in the IMU. To the best of our knowledge, our system is the first to follow this integration approach, this being one of the main contributions of this work. Having validated the system with handheld testing, flight tests were performed. We show, qualitatively, that our system effectively yields improved trajectory estimates under low GPS signal reception.

Aerial Robots On the Way to Underground: An Experimental Evaluation of VINSMono on VisualInertial Odometry Camera In recent years, applications of unmanned aerial vehicles (UAV) have been well received by a wide range of users due to its versatility and costeffectiveness. When the UAVs operate outside without any surroundings, it can be possible to integrate global positioning system (GPS) into the UAV. However, GPS is ineffective to determine the position of UAV when the signal is weak or absent, such as operating in an indoor environment and tunnel network. To accurately determine the position of a UAV, another approach is to endow different sensors such as LIDAR, inertial measurement unit (IMU) and camera. In this paper, we present an experimental evaluation of a visualinertial odometry application using VINSMono state estimator coupled with visualinertial sensors. The objective of the experiment is to determine the effectiveness of the setup in an indoor environment, where the GPS signal is fully absent. In the experiment, a UAV capable of selflocalization has been constructed with the integration of onboard computer, camera, and IMU. First, camera and IMU calibrations, as well as their hardware synchronization, have been set. Second, experimental evaluations have conducted to compare the UAV localization performance with ground truth data at motion analysis laboratory equipped with OptiTrack tracking system. Finally, a comparison study is given to determine the robustness and reliability of VINSMono state estimator and the UAV system using various flight velocities and environment features settings. Based on extensive evaluations, it is capable to localize a UAV in a GPSdenied environment, with an average root mean square error kept under 25 cm over an average flight duration within 2 minutes.