Vision-Based Precision Landings of a Tailsitter UAV

Millet, Paul Travis

doi:10.2514/6.2009-5680

Cited by 6 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, more sensors need to be added to the aircraft to achieve the navigation of autonomous flight. When incorporating GPS navigation and vision-based navigation [8], UAV will be controlled automatically taking off and landing in the urban environment, and complete the task of trajectory tracking and indoor vertical flight.…”

Section: Discussionmentioning

confidence: 99%

Lift System Design of Tail-Sitter Unmanned Aerial Vehicle

Zhang¹,

Chen²,

Lv³

2012

ICA

View full text Add to dashboard Cite

The main advantage of tail-sitter unmanned aerial vehicle (UAV) are introduced. Three design solutions of rotor tail-sitter lift system of UAV have been presented and the respective control strategies and characteristics of three solutions are also analyzed in the paper, through the related experiments the design of twin-rotor lift system is verified, and its feasibility is proved. The characteristics and the applying background of the twin-rotor tail-sitter UAV are described in detail. Some useful conclusions of the lift system for tail-sitter UAV are obtained

show abstract

Section: Discussionmentioning

confidence: 99%

Lift System Design of Tail-Sitter Unmanned Aerial Vehicle

Zhang¹,

Chen²,

Lv³

2012

ICA

View full text Add to dashboard Cite

show abstract

“…For instance, Saripalli et al [12] and Merz et al [13] have both successfully landed a helicopter detecting and tracking a predefined landmark using vision. Some of the contributions have targeted controlled landing using estimations from images [14], and particularly optic flow [15], however the landing site is arbitrarily selected.…”

Section: Introductionmentioning

confidence: 99%

A multi-layered approach for site detection in UAS emergency landing scenarios using geometry-based image segmentation

Mejias

Fitzgerald²

2013

2013 International Conference on Unmanned Aircraft Systems (ICUAS)

View full text Add to dashboard Cite

This paper presents an alternative approach to image segmentation by using the spatial distribution of edge pixels as opposed to pixel intensities. The segmentation is achieved by a multi-layered approach and is intended to find suitable landing areas for an aircraft emergency landing. We combine standard techniques (edge detectors) with novel developed algorithms (line expansion and geometry test) to design an original segmentation algorithm. Our approach removes the dependency on environmental factors that traditionally influence lighting conditions, which in turn have negative impact on pixel-based segmentation techniques. We present test outcomes on realistic visual data collected from an aircraft, reporting on preliminary feedback about the performance of the detection. We demonstrate consistent performances over 97% detection rate.

show abstract

“…The authors in [21] use video from the onboard camera and the orientation and altitude of the ground vehicle to calculate the UAV position with respect to (w.r.t.) the target.…”

Section: Introductionmentioning

confidence: 99%

Artificial Marker and MEMS IMU-Based Pose Estimation Method to Meet Multirotor UAV Landing Requirements

Niu

et al. 2019

Sensors

View full text Add to dashboard Cite

The fully autonomous operation of multirotor unmanned air vehicles (UAVs) in many applications requires support of precision landing. Onboard camera and fiducial marker have been widely used for this critical phase due to its low cost and high effectiveness. This paper proposes a six-degrees-of-freedom (DoF) pose estimation solution for UAV landing based on an artificial marker and a micro-electromechanical system (MEMS) inertial measurement unit (IMU). The position and orientation of the landing maker are measured in advance. The absolute position and heading of the UAV are estimated by detecting the marker and extracting corner points with the onboard monocular camera. To achieve continuous and reliable positioning when the marker is occasionally shadowed, IMU data is fused by an extended Kalman filter (EKF). The error terms of the IMU sensor are modeled and estimated. Field experiments show that the positioning accuracy of the proposed system is at centimeter level, and the heading error is less than 0.1 degrees. Comparing to the marker-based approach, the roll and pitch angle errors decreased by 33% and 54% on average. Within five seconds of vision outage, the average drifts of the horizontal and vertical position were 0.41 and 0.09 m, respectively.

show abstract

Vision-Based Precision Landings of a Tailsitter UAV

Cited by 6 publications

References 14 publications

Lift System Design of Tail-Sitter Unmanned Aerial Vehicle

Lift System Design of Tail-Sitter Unmanned Aerial Vehicle

A multi-layered approach for site detection in UAS emergency landing scenarios using geometry-based image segmentation

Artificial Marker and MEMS IMU-Based Pose Estimation Method to Meet Multirotor UAV Landing Requirements

Contact Info

Product

Resources

About