Observability Analysis of Position Estimation for Quadrotors With Modified Dynamics and Range Measurements This study performs a nonlinear observability analysis on range assisted inertial navigation system (INS) for quadrotor microaerial vehicles (MAV). The INS is formulated incorporating the quadrotor dynamics with aerodynamic drag forces. The observability analysis is carried out for cases where three and two range measurements are available. The analysis facilitates the range assisted localization of MAVs when there are less than four range measurements are available. The primary objective of this study is to identify the conditions under which the INS becomes unobservable, and these conditions are validated through numerical simulation. The main contributions of this paper are as follows, 1. Nonlinear observability analysis of the range assisted INS for quadrotor MAVs. 2. Theoretical derivation and numerical validation of unobservable conditions for three and two range cases. 3. Experimental validations of estimator performance.

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.