A 3 Points Vision Based Approach for MAV Localization in GPS Denied Environments

International audienceThis paper introduces a new method to localize a micro aerial vehicle (MAV) in GPS denied environments and without the usage of any known pattern. The method exploits the planar ground assumption and only uses the data provided by a monocular camera and an inertial measurement unit. It is based on a closed solution which provides the vehicle pose from a single camera image, once the roll and the pitch angles are obtained by the inertial measurements. Specifically, the vehicle position and attitude can uniquely be determined by having two point features. However, the precision is significantly improved by using three point features. The closed form solution makes the method very simple in terms of computational cost and therefore very suitable for real time implementation. Additionally, because of this closed solution, the method does not need any initialization. Results of experimentation show the effectiveness of the proposed approach

Obstacle detection through sensor fusion is unmanned surface vehicles

In our diploma thesis we issue a navigation problem of autonomous surface vehicles. Presume we have a vessel appointed with sensors, such as GPS, IMU, compass, a stereo sistem of two cameras and a standalone processing unit that combines sensor data and performs segmentation and planning in real time. We begin by introducing the algorithm of segmentation, that along with help of advanced computer vision methods extracts useful visual information, therefore avoids obstacles in its path (boats, swimmers, buoys). Then, we focus on horizon estimation by taking into account data of innertial measurement unit, with whom we improve the estimation of a sea border. To improve localization of obstacles in front of the vessel, we calculate depth of every pixel in the image. Image pairs are first rectified as we simplify the correspondence search to single dimension. Furthermore, we present, implement and evaluate our methods. We conclude by discussing further work that can be done

Fixed-wing MAV attitude stability in atmospheric turbulence, part 1: Suitability of conventional sensors

Fixed-wing Micro-Aerial Vehicles (MAVs) need effective sensors that can rapidly detect turbulence induced motion perturbations. Current MAV attitude control systems rely on inertial sensors. These systems can be described as reactive; detecting the disturbance only after the aircraft has responded to the disturbing phenomena. In this part of the paper, the current state of the art in reactive attitude sensing for fixed-wing MAVs are reviewed. A scheme for classifying the range of existing and emerging sensing techniques is presented. The features and performance of the sensing approaches are discussed in the context of their application to MAV attitude control systems in turbulent environments. It is found that the use of single sensors is insufficient for MAV control in the presence of turbulence and that potential gains can be realised from multi-sensor systems. A successive paper to be published in this journal will investigate novel attitude sensors which have the potential to improve attitude control of MAVs in Turbulenc

Visual-inertial UAV attitude estimation using urban scene regularities

Abstract — We present a drift-free attitude estimation method that uses image line segments for the correction of accumulated errors in integrated gyro rates when an unmanned aerial vehicle (UAV) operates in urban areas. Since man-made environments generally exhibit strong regularity in structure, a set of line segments that are either parallel or orthogonal to the gravitational direction can provide visual measurements for the absolute attitude from a calibrated camera. Line segments are robustly classified with the assumption that a single vertical vanishing point or multiple horizontal vanishing points exist. In the fusion with gyro angles, we introduce a new Kalman update step that directly uses line segments rather than vanishing points. The simulation and experiment based on urban images at distant views are provided to demonstrate that our method can serve as a robust visual attitude sensor for aerial robot navigation. I

Phase-advanced attitude sensing and control for fixed-wing micro aerial vehicles in turbulence

The scale of fixed-wing Micro Aerial Vehicles (MAVs) lend them to many unique applications. These applications often require low speed flights close to the ground, in the vicinity of large obstacles and in the wake of buildings. A particular challenge for MAVs is attitude control in the presence of high turbulence. Such flights pose a challenging operational environment for MAVs, and in particular, ensuring sufficient attitude control in the presence of significant turbulence. Low-level flight in the atmospheric boundary layer without sufficient attitude control is hazardous, mainly due to the high levels of turbulence intensity close to the ground. MAV accidents have occurred due to the lack of a reliable attitude control system in turbulent conditions as reported in the literature. Challenges associated with flight control of fixed-wing MAVs operating in complex environments are significantly different to any larger scale vehicle. The scale of MAVs makes them particularly sensitive to atmospheric disturbances thus limiting their operation. A review of the literature revealed that rolling inputs from turbulence were the most challenging whereby conventional inertial-based attitude control systems lack the responsiveness for roll control in high turbulence environments. The solution might lie with flying animals, which have adapted to flight within turbulence. The literature survey identified bio-inspired phase-advanced sensors as a promising sensory solution for complementing current reactive attitude sensors. The development of a novel bio-inspired phase-advanced sensor and associated control system, which can sense the flow disturbances before an attitude perturbation, is the focus of this research. The development of such a system required an in-depth understanding of the features of the disturbing phenomena; turbulence. Correlation studies were conducted between the oncoming turbulence and wing-surface pressure variations. It was found that the highest correlation exists between upstream flow pitch angle variation and the wing-surface pressure fluctuations. However, due to the insufficient time-forward advantage, surface pressure sensing was not used for attitude control. A second sensing approach was explored to cater for the control system’s time-lags. Multi-hole pressure probes were embedded in the wings of the MAV to sense flow pitch angle and magnitude variation upstream of the wing. The sensors provide an estimate of the disturbing turbulence. This approach caters for the time-lags of the system providing sufficient time to counteract the gust before it results in an inertial response. Statistical analysis was used to assess the disturbance rejection performance of the phase-advanced sensory system, which was benchmarked against a conventional inertial-based sensory system in a range of turbulence conditions. Unconstrained but controlled test flights were conducted inside the turbulence environment of two wind-tunnels, in addition to outdoor flight testing in the atmosphere. These three different turbulence conditions enabled testing of a wide range of turbulence spectra believed to be most detrimental to the MAV. A significant improvement in disturbance rejection performance was observed in relation to conventional inertial-based sensory systems. It can be concluded that sensory systems providing time-forward estimates of turbulence can complement conventional inertial-based sensors to improve the attitude stability performance

Location-Based Sensor Fusion for UAS Urban Navigation.

For unmanned aircraft systems (UAS) to effectively conduct missions in urban environments, a multi-sensor navigation scheme must be developed that can operate in areas with degraded Global Positioning System (GPS) signals. This thesis proposes a sensor fusion plug and play capability for UAS navigation in urban environments to test combinations of sensors. Measurements are fused using both the Extended Kalman Filter (EKF) and Ensemble Kalman Filter (EnKF), a type of Particle Filter. A Long Term Evolution (LTE) transceiver and computer vision sensor each augment the traditional GPS receiver, inertial sensors, and air data system. Availability and accuracy information for each sensor is extracted from the literature. LTE positioning is motivated by a perpetually expanding network that can provide persistent measurements in the urban environment. A location-based logic model is proposed to predict sensor availability and accuracy for a given type of urban environment based on a map database as well as real-time sensor inputs and filter outputs. The simulation is executed in MATLAB where the vehicle dynamics, environment, sensors, and filters are user-customizable. Results indicate that UAS horizontal position accuracy is most dependent on availability of high sampling rate position measurements along with GPS measurement availability. Since the simulation is able to accept LTE sensor specifications, it will be able to show how the UAS position accuracy can be improved in the future with this persistent measurement, even though the accuracy is not improved using current LTE state-of-the-art. In the unmatched true propagation and filter dynamics model scenario, filter tuning proves to be difficult as GPS availability varies from urban canyon to urban canyon. The main contribution of this thesis is the generation of accuracy data for different sensor suites in both a homogeneous urban environment (solid walls) using matched dynamics models and a heterogeneous urban environment layout using unmatched models that necessitate filter tuning. Future work should explore the use of downward facing VISION sensors and LiDAR, integrate real-time map information into sensor availability and measurement weighting decisions, including the use of LTE for approximate localization, and more finely represent expected measurement accuracies in the GPS and LTE networks.PhDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/110361/1/jrufa_1.pd

Computer vision-based localization and mapping of an unknown, uncooperative and spinning target for spacecraft proximity operations

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2013.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 399-410).Prior studies have estimated that there are over 100 potential target objects near the Geostationary Orbit belt that are spinning at rates of over 20 rotations per minute. For a number of reasons, it may be desirable to operate in close proximity to these objects for the purposes of inspection, docking and repair. Many of them have an unknown geometric appearance, are uncooperative and non-communicative. These types of characteristics are also shared by a number of asteroid rendezvous missions. In order to safely operate in close proximity to an object in space, it is important to know the target object's position and orientation relative to the inspector satellite, as well as to build a three-dimensional geometric map of the object for relative navigation in future stages of the mission. This type of problem can be solved with many of the typical Simultaneous Localization and Mapping (SLAM) algorithms that are found in the literature. However, if the target object is spinning with signicant angular velocity, it is also important to know the linear and angular velocity of the target object as well as its center of mass, principal axes of inertia and its inertia matrix. This information is essential to being able to propagate the state of the target object to a future time, which is a key capability for any type of proximity operations mission. Most of the typical SLAM algorithms cannot easily provide these types of estimates for high-speed spinning objects. This thesis describes a new approach to solving a SLAM problem for unknown and uncooperative objects that are spinning about an arbitrary axis. It is capable of estimating a geometric map of the target object, as well as its position, orientation, linear velocity, angular velocity, center of mass, principal axes and ratios of inertia. This allows the state of the target object to be propagated to a future time step using Newton's Second Law and Euler's Equation of Rotational Motion, and thereby allowing this future state to be used by the planning and control algorithms for the target spacecraft. In order to properly evaluate this new approach, it is necessary to gather experiby Brent Edward Tweddle.Ph. D