Amir Melzer

Robust state estimation for small unmanned airplanes

As a basis for autonomous operation, Unmanned Aerial Systems (UAS) require an on-board state estimation that achieves both high accuracy as well as robustness with respect to certain conditions. We present a multi-sensor fusion framework based on Extended Kalman Filtering (EKF) which is light-weight enough to run on-board small unmanned airplanes using measurements from a MEMS based Inertial Measurement Unit (IMU), static and dynamic pressure sensors, as well as GPS (position and velocity) and a 3D magnetic compass. The on-board state estimator continuously estimates position, velocity, attitude and heading, IMU biases as well as the 3D wind vector in a tightly-coupled manner. In addition, airplane Angle of Attack (AoA) as well as sideslip angle can be derived by involving an aerodynamics model. The resulting infrastructure allows for unbiased orientation, airspeed and AoA tracking even in the case of GPS outages over extended periods of time. It can furthermore detect and reject outliers in sensor readings using Mahalanobis distance checks. We validate the proposed method with flight data from a small unmanned airplane: we demonstrate robustness w.r.t. outliers and GPS outages by disabling or corrupting respective flight data in post processing analyses.

Long-Endurance Sensing and Mapping Using a Hand-Launchable Solar-Powered UAV

This paper investigates and demonstrates the potential for very long endurance autonomous aerial sensing and mapping applications with AtlantikSolar, a small-sized, hand-launchable, solar-powered fixed-wing unmanned aerial vehicle. The platform design as well as the on-board state estimation, control and path-planning algorithms are overviewed. A versatile sensor payload integrating a multi-camera sensing system, extended on-board processing and high-bandwidth communication with the ground is developed. Extensive field experiments are provided including publicly demonstrated field-trials for search-and-rescue applications and long-term mapping applications. An endurance analysis shows that AtlantikSolar can provide full-daylight operation and a minimum flight endurance of 8 h throughout the whole year with its full multi-camera mapping payload. An open dataset with both raw and processed data is released and accompanies this paper contribution.

Maximum Likelihood Identification of Inertial Sensor Noise Model Parameters

Accurate visual-inertial localization and mapping systems require accurate calibration and good sensor error models. To this end, we present a simple offline method to automatically determine the parameters of inertial sensor noise models. The proposed methodology identifies noise processes across a large range of strength and time-scales, for example, weak gyroscope bias fluctuations buried in broadband noise. This is accomplished with a classical maximum likelihood estimator, based on the integrated process (i.e., the angle, velocity, or position), rather than on the angular rate or acceleration as is standard in the literature. This trivial modification allows us to capture noise processes according to their effect on the integrated process, irrespective of their contribution to rate or acceleration noise. The cause of the noise is not discussed in this article. The method is tested on different classes of sensors by automatically identifying the parameters of a standard inertial sensor noise model. The results are analyzed qualitatively by comparing the models Allan variance to the Allan variance computed directly from sensor data. A simulation that resembles one of the devices under test facilitates a quantitative analysis of the proposed estimator. Comparison with a competing, state-of-the-art method shows the advantages of the algorithm.

Robotic technologies for solar-powered UAVs: Fully autonomous updraft-aware aerial sensing for multiday search-and-rescue missions

Large-scale aerial sensing missions can greatly benefit from the perpetual endurance capability provided by high-performance low-altitude solar-powered UAVs. However, today these UAVs suffer from small payload capacity, low energetic margins and high operational complexity. To tackle these problems, this paper presents four individual technical contributions and integrates them into an existing solar-powered UAV system: First, a lightweight and power-efficient day/night-capable sensing system is discussed. Second, means to optimize the UAV platform to the specific payload and to thereby achieve sufficient energetic margins for day/night-flight with payload are presented. Third, existing autonomous launch and landing functionality is extended for solar-powered UAVs. Fourth, as a main contribution an extended Kalman filter-based autonomous thermal updraft tracking framework is developed. Its novelty is that it allows the end-to-end integration of the thermal-induced roll moment into the estimation process. It is assessed against unscented Kalman filter and particle filter methods in simulation and implemented on the aircraft’s low-power autopilot. The complete system is verified during a 26-hour search-and-rescue aerial sensing mockup mission that represents the first-ever fully-autonomous perpetual endurance flight of a small solar-powered UAV with a day/night-capable sensing payload. It also represents the first time that solar-electric propulsion and autonomous thermal updraft tracking are combined in flight. In contrast to previous work that has focused on the energetic feasibility of perpetual flight, the individual technical contributions of this paper are considered core functionality to guarantee ease-of-use, effectivity and reliability in future multi-day aerial sensing operations with small solar-powered UAVs.

Model-based wind estimation for a hovering VTOL tailsitter UAV

To many unmanned aerial vehicle (UAV) designs, the lack of information about the wind speed and direction is a limiting factor in achieving robust outdoor flight. This paper addresses the problem of wind estimation onboard a hovering vertical take-off and landing (VTOL) tailsitter UAV. The proposed estimation framework makes use of the standard onboard sensor suite: inertial measurement unit (IMU), global positioning system (GPS) and a magnetometer. No additional airspeed sensor is needed. As a result, the autopilot is provided with an estimate of the wind velocity vector in the horizontal (north-east) plane. An aerodynamic model of the vehicle has been derived and used in a Kalman filter framework to estimate the horizontal wind velocity vector in real-time. The wind estimator has been implemented onboard the UAVs autopilot and validated in real flight. As a result, we successfully obtain the direction and speed of the wind with an estimation accuracy close to the accuracy range of the ground truth measurement. Furthermore, the derived grey-box model allows to generalise the framework to different airframes.

Robust map generation for fixed-wing UAVs with low-cost highly-oblique monocular cameras

Accurate and robust real-time map generation onboard of a fixed-wing UAV is essential for obstacle avoidance, path planning, and critical maneuvers such as autonomous take-off and landing. Due to the computational constraints, the required robustness and reliability, it remains a challenge to deploy a fixed-wing UAV with an online-capable, accurate and robust map generation framework. While photogrammetric approaches have underlying assumptions on the structure and the view of the camera, generic simultaneous localization and mapping (SLAM) approaches are computationally demanding. This paper presents a framework that uses the autopilots state estimate as a prior for sliding window bundle adjustment and map generation. Our approach outputs an accurate geo-referenced dense point-cloud which was validated in simulation on a synthetic dataset and on two real-world scenarios based on ground control points.