Drones are becoming increasingly significant for vast applications, such as firefighting, and rescue. While flying in challenging environments, reliable Global Navigation Satellite System (GNSS) measurements cannot be guaranteed all the time, and the Inertial Navigation System (INS) navigation solution will deteriorate dramatically. Although different aiding sensors, such as cameras, are proposed to reduce the effect of these drift errors, the positioning accuracy by using these techniques is still affected by some challenges, such as the lack of the observed features, inconsistent matches, illumination, and environmental conditions. This paper presents an integrated navigation system for Unmanned Aerial Vehicles (UAVs) in GNSS denied environments based on a Radar Odometry (RO) and an enhanced Visual Odometry (VO) to handle such challenges since the radar is immune against these issues. The estimated forward velocities of a vehicle from both the RO and the enhanced VO are fused with the Inertial Measurement Unit (IMU), barometer, and magnetometer measurements via an Extended Kalman Filter (EKF) to enhance the navigation accuracy during GNSS signal outages. The RO and VO are integrated into one integrated system to help overcome their limitations, since the RO measurements are affected while flying over non-flat terrain. Therefore, the integration of the VO is important in such scenarios. The experimental results demonstrate the proposed system’s ability to significantly enhance the 3D positioning accuracy during the GNSS signal outage.

Velocity updates have been proven to be important for constraining motion-sensor-based dead-reckoning (DR) solutions in indoor unmanned aerial vehicle (UAV) applications. The forward velocity from a mass flow sensor and the lateral and vertical non-holonomic constraints (NHC) can be utilized for three-dimensional (3D) velocity updates. However, it is observed that (a) the quadrotor UAV may have a vertical velocity trend when it is controlled to move horizontally; (b) the quadrotor may have a pitch angle when moving horizontally; and (c) the mass flow sensor may suffer from sensor errors, especially the scale factor error. Such phenomenons degrade the performance of velocity updates. Thus, this paper presents a multi-sensor integrated localization system that has more effective sensor interactions. Specifically, (a) the barometer data are utilized to detect height changes and thus determine the weight of vertical velocity update; (b) the pitch angle from the inertial measurement unit (IMU) and magnetometer data fusion is used to set the weight of forward velocity update; and (c) an extra mass flow sensor calibration module is introduced. Indoor flight tests have indicated the effectiveness of the proposed sensor interaction strategies in enhancing indoor quadrotor DR solutions, which can also be used for detecting outliers in external localization technologies such as ultrasonics.

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This paper presents a novel approach to enhance unmanned aerial vehicle (UAV) autonomous navigation, without adding extra load to the vehicle. The proposed approach employs the UAV vehicle dynamic model to aid the navigation estimation in a Global Navigation Satellite Systems (GNSS)‐denied environment, without the need to model any part of the UAV, and avoids the requirement for special equipment during the modeling procedures typically required for vehicle dynamic model‐aided navigation. Taking advantage of the available information from previous flights during availability of GNSS, and with the aid of a hybrid machine learning approach, the proposed technique is able to enhance the navigation accuracy during GNSS outage, despite the massive drift occurring from utilizing a low‐cost inertial measurement unit (IMU) during the outage period. Different scenarios are investigated to prove the robustness of the proposed technique, and the results are compared to two types of IMUs with the aid of an inverse mechanization IMU simulator. © 2018 Institute of Navigation

<p><strong>Abstract.</strong> Autonomous Unmanned Aerial Vehicles (UAVs) have drawn great attention from different organizations, because of the various applications that save time, cost, effort, and human lives. The navigation of autonomous UAV mainly depends on the fusion between Global Navigation Satellite System (GNSS) and Inertial Measurement System (IMU). Navigation in indoor environments is a challenging task, because of the GNSS signal unavailability, especially when the utilized IMU is low-cost. Light Detection and Ranging Radar (LIDAR) is one of the mainly utilized sensors in the indoor environment for localization through scan matching of successive scans. The process of calculating the rotation and translation from successive scans can employ different approaches, such as Iterative Closest Point (ICP) with its variants, and Hector SLAM. ICP and Hector SLAM iterative fashion can greatly increase the matching time, and the convergence is not guaranteed in case of harsh maneuvers, moving objects, and short-range LIDAR as it may get stuck in local minima. This paper proposes enhanced real-time ICP and Hector SLAM algorithms based on vehicle model (VM) during sharp maneuvers. The vehicle model serves as initialization step (coarse alignment) then the ICP/Hector serve as fine alignment step. Test cases of quadcopter flight with harsh maneuvers were carried out with LIDAR to evaluate the proposed approach to enhance the ICP/Hector convergence time and accuracy. The proposed algorithm is convenient for UAVs where there are limitations regarding the size, weight, and power limitations, as it is a stand-alone algorithm that does not require any additional sensors.</p>