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.

A GPSaided Omnidirectional VisualInertial State Estimator in Ubiquitous Environments The visualinertial navigation system (VINS) has been a practical approach for state estimation in recent years. In this paper, we propose a general GPSaided omnidirectional visualinertial state estimator capable of operating in ubiquitous environments and platforms. Our system consists of two parts: 1) the preprocessing of omnidirectional cameras, IMU, and GPS measurements, and 2) the sliding window based nonlinear optimization for accurate state estimation. We test our system in different conditions including an indoor office, campus roads, and challenging open water surface. Experiment results demonstrate the high accuracy of our approach than stateoftheart VINSs in all scenarios. The proposed odometry achieves drift ratio less than 0.5% in 1200 m length outdoors campus road in overexposure conditions and 0.65% in open water surface, without a loop closure, compared with a centimeter accuracy GPS reference.