Hybrid IMUAided Approach for Optimized Visual Odometry Autonomous navigation of unmanned vehicles in GPSdenied environments is a challenging problem, especially for small ground vehicles and micro aerial vehicles (MAVs) which are characterized by their small payload, short battery lifetime and limited processing resources. Stereo vision positioning has been introduced as a scalefree positioning technique, but it is computationally expensive. Monocular vision systems aided by inertial measurement unit (IMU) are more computationally efficient but it suffers from IMU random biases and scale errors. In this paper, we propose a hybrid visualinertial odometry solution that minimizes the computation load by dividing the mission into two interchangeable stages. Firstly, a stereo vision stage in which a loosely coupled integration between stereo cameras and IMU is performed. In this stage, an extended Kalman filter (EKF) is used to automatically and dynamically estimate IMU biases. Once the IMU is calibrated, a monocular stage is activated where the system is downgraded into single camera getting the motion scale from the calibrated IMU. The proposed solution has been tested using the popular IMUenabled ZEDMini tracking camera. We compared our stereo vision solution against the IMUaided monocular solution and the results showed accurate positioning with the advantage of less computation. Further analysis is provided where we compared our solution with the builtin solutions of the ZED Mini camera and the Intel Realsense T265 tracking camera.

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.