This paper considers the problem of obtaining good attitude estimates from measurements obtained from typical low cost inertial measurement units. The outputs of such systems are characterized by high noise levels and time varying additive biases. We formulate the filtering problem as deterministic observer kinematics posed directly on the special orthogonal group SO (3) driven by reconstructed attitude and angular velocity measurements. Lyapunov analysis results for the proposed observers are derived that ensure almost global stability of the observer error. The approach taken leads to an observer that we term the direct complementary filter. By exploiting the geometry of the special orthogonal group a related observer, termed the passive complementary filter, is derived that decouples the gyro measurements from the reconstructed attitude in the observer inputs. Both the direct and passive filters can be extended to estimate gyro bias online. The passive filter is further developed to provide a formulation in terms of the measurement error that avoids any algebraic reconstruction of the attitude. This leads to an observer on SO(3), termed the explicit complementary filter, that requires only accelerometer and gyro outputs; is suitable for implementation on embedded hardware; and provides good attitude estimates as well as estimating the gyro biases online. The performance of the observers are demonstrated with a set of experiments performed on a robotic test-bed and a radio controlled unmanned aerial vehicle.

/pdf/nonlinear-complementary-filters-on-the-special-orthogonal-ysp0zzaoar.pdf

Nonlinear Complementary Filters on the Special Orthogonal Group

The days where swarms of unmanned aerial vehicles (UAVs) will occupy our skies are fast approaching due to the introduction of cost-efficient and reliable small aerial vehicles and the increasing demand for use of such vehicles in a plethora of civil applications. Governments and industry alike have been heavily investing in the development of UAVs. As such it is important to understand the characteristics of networks with UAVs to enable the incorporation of multiple, coordinated aerial vehicles into the air traffic in a reliable and safe manner. To this end, this survey reports the characteristics and requirements of UAV networks for envisioned civil applications over the period 2000–2015 from a communications and networking viewpoint. We survey and quantify quality-of-service requirements, network-relevant mission parameters, data requirements, and the minimum data to be transmitted over the network. Furthermore, we elaborate on general networking related requirements such as connectivity, adaptability, safety, privacy, security, and scalability. We also report experimental results from many projects and investigate the suitability of existing communication technologies for supporting reliable aerial networking.

Survey on Unmanned Aerial Vehicle Networks for Civil Applications: A Communications Viewpoint

Mobile 3D mapping for surveying earthwork projects using an Unmanned Aerial Vehicle (UAV) system

This paper presents two types of nonlinear controllers for an autonomous quadrotor helicopter. One type, a feedback linearization controller involves high-order derivative terms and turns out to be quite sensitive to sensor noise as well as modeling uncertainty. The second type involves a new approach to an adaptive sliding mode controller using input augmentation in order to account for the underactuated property of the helicopter, sensor noise, and uncertainty without using control inputs of large magnitude. The sliding mode controller performs very well under noisy conditions, and adaptation can effectively estimate uncertainty such as ground effects.

Feedback linearization vs. adaptive sliding mode control for a quadrotor helicopter

Remotely Piloted Aircraft (RPA) is presently in continuous development at a rapid pace. Unmanned Aerial Vehicles (UAVs) or more extensively Unmanned Aerial Systems (UAS) are platforms considered under the RPAs paradigm. Simultaneously, the development of sensors and instruments to be installed onboard such platforms is growing exponentially. These two factors together have led to the increasing use of these platforms and sensors for remote sensing applications with new potential. Thus, the overall goal of this paper is to provide a panoramic overview about the current status of remote sensing applications based on unmanned aerial platforms equipped with a set of specific sensors and instruments. First, some examples of typical platforms used in remote sensing are provided. Second, a description of sensors and technologies is explored which are onboard instruments specifically intended to capture data for remote sensing applications. Third, multi-UAVs in collaboration, coordination, and cooperation in remote sensing are considered. Finally, a collection of applications in several areas are proposed, where the combination of unmanned platforms and sensors, together with methods, algorithms, and procedures provide the overview in very different remote sensing applications. This paper presents an overview of different areas, each independent from the others, so that the reader does not need to read the full paper when a specific application is of interest

Overview and Current Status of Remote Sensing Applications Based on Unmanned Aerial Vehicles (UAVs)

A UAV for bridge inspection: Visual servoing control law with orientation limits

Attitude and gyro bias estimation for a VTOL UAV

This paper describes a visual tracking control law of an Unmanned Aerial Vehicle (UAV) for monitoring of structures and maintenance of bridges. It presents a control law based on computer vision for quasi-stationary flights above a planar target. The first part of the UAV's mission is the navigation from an initial position to a final position to define a desired trajectory in an unknown 3D environment. The proposed method uses the homography matrix computed from the visual information and derives, using backstepping techniques, an adaptive nonlinear tracking control law allowing the effective tracking and depth estimation. The depth represents the desired distance separating the camera from the target.

Visual Tracking Control of Aerial Robotic Systems with Adaptive Depth Estimation

In this paper, a nonlinear complimentary filter (x-estimator) is presented to estimate the attitude of a UAV (unmanned aerial vehicle). The measurements are taken from a low-cost SMU (inertial measurement unit) which consists of 3-axis accelerometers and 3-axis gyroscopes. The gyro bias are estimated online. A second nonlinear complimentary filter (z-estimator) is also designed, it combines 3-axis gyroscope readings with 3-axis magnetometer measurements. From the proposed estimators, the full rotation matrix R will be retrieved. Both estimators use the fact that the orientation matrix, evolving on SO(3), is not locally parameterized and thus could be used to describe any kind of 3D motion. Convergence of the two observers is theoretically proved and simulations as well as experiments are conducted on a real platform in hovering flight conditions.

Attitude and gyro bias estimation for a flying UAV

This paper describes a visual tracking control law of an Unmanned Aerial Vehicle (UAV) for monitoring of structures and maintenance of bridges. It presents a control law based on computer vision for quasi-stationary flights above a planar target. The first part of the UAV’s mission is the navigation from an initial position to a final position to define a desired trajectory in an unknown 3D environment. The proposed method uses the homography matrix computed from the visual information and derives, using backstepping techniques, an adaptive nonlinear tracking control law allowing the effective tracking and depth estimation. The depth represents the desired distance separating the camera from the target.

/pdf/visual-tracking-control-of-aerial-robotic-systems-with-2zslb76b3g.pdf

Najib Metni

Papers

A UAV for bridge inspection: Visual servoing control law with orientation limits

Attitude and gyro bias estimation for a VTOL UAV

Visual Tracking Control of Aerial Robotic Systems with Adaptive Depth Estimation

Attitude and gyro bias estimation for a flying UAV

Visual Tracking Control of Aerial Robotic Systems with Adaptive Depth Estimation