Unmanned Aerial Vehicle Navigation Using Wide-Field Optical Flow and Inertial Sensors

Rhudy, Matthew B.; Gu, Yu; Huo, Chao; Gross, Jason N.

doi:10.1155/2015/251379

Cited by 16 publications

(8 citation statements)

References 32 publications

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“…Each image is input to an arbitrary optical flow algorithm that estimates pixel shift

\overset{α}{true}

and

\overset{β}{true}

between consecutive frames. For this configuration, it has been shown that given the focal length of the sensor optics

f

, the roll and pitch attitude of the camera

ϕ_{c}

and

θ_{c}

, the body frame angular rates

p, q, r

velocity components

u, v, w

, and range to observed texture

η_{z}

, then the optical flow estimate is given by the following equation (Chao et al, ; Rhudy, Gu, Chao, & Gross, ):

true[\begin{matrix} center \dot{α} \\ center \dot{β} \end{matrix} true] = \frac{f + β \tan θ_{normalc} - α \tan ϕ_{normalc} ∕ \cos θ_{normalc}}{η_{z}} true[\begin{matrix} center - u + \frac{α ω}{f} \\ center - v + \frac{β ω}{f} \end{matrix} true] - true[\begin{matrix} center f q - r β - \frac{p α v + q α^{2}}{f} \\ center - f p + r α - \frac{p β^{2} + q α β}{f} \end{matrix} true] .

…”

Section: Theorymentioning

confidence: 99%

Reducing the complexity of visual navigation: Optical track controller for long‐range unmanned aerial vehicles

Rosser

Chahl

2019

Journal of Field Robotics

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Using vision for navigation of airborne systems provides an opportunity for motion relative to the ground to be controlled in the absence of other supporting sensors including global navigation satellite systems. Rather than relying on computationally intensive localization techniques such as online map construction, identification and tracking of landmarks, or otherwise producing an explicit quantitative estimate of position, we propose and have experimentally demonstrated a closed-loop visual navigation reflex which we term the optical ground course controller. The behavior is applicable to fixed wing aircraft traversing long ranges, and reduces the online computation and sensors required compared to other visual methods. This method combines the kinematics of fixed wing aircraft flight, the direction of apparent motion of an image sequence, and a magnetic compass to create a bioinspired optomotor reflex similar to those observed in insects. This behavior accurately controls track in the inertial reference frame (path taken over the ground) with only limited dependence on altitude, speed, and wind. We show that the proposed behavior is naturally convergent and stable, and present experimental results from simulation and real-world flight demonstrating that the method performs robustly, producing improvement over both magnetic-referenced and visual odometry-based navigation within the limits of the sensor. K E Y W O R D S embodied autonomy, optical flow, optomotor, UAV

show abstract

“…Each image is input to an arbitrary optical flow algorithm that estimates pixel shift

\overset{α}{true}

and

\overset{β}{true}

between consecutive frames. For this configuration, it has been shown that given the focal length of the sensor optics

f

, the roll and pitch attitude of the camera

ϕ_{c}

and

θ_{c}

, the body frame angular rates

p, q, r

velocity components

u, v, w

, and range to observed texture

η_{z}

, then the optical flow estimate is given by the following equation (Chao et al, ; Rhudy, Gu, Chao, & Gross, ):

true[\begin{matrix} center \dot{α} \\ center \dot{β} \end{matrix} true] = \frac{f + β \tan θ_{normalc} - α \tan ϕ_{normalc} ∕ \cos θ_{normalc}}{η_{z}} true[\begin{matrix} center - u + \frac{α ω}{f} \\ center - v + \frac{β ω}{f} \end{matrix} true] - true[\begin{matrix} center f q - r β - \frac{p α v + q α^{2}}{f} \\ center - f p + r α - \frac{p β^{2} + q α β}{f} \end{matrix} true] .

…”

Section: Theorymentioning

confidence: 99%

Reducing the complexity of visual navigation: Optical track controller for long‐range unmanned aerial vehicles

Rosser

Chahl

2019

Journal of Field Robotics

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“…The Phastball SUAV airframe [43] was developed as a modular research platform and has been used for multiple sensorfusion studies [44][45][46]. The Phastball Zero SUAV is shown in Figure 3.…”

Section: Experimental Set-upmentioning

confidence: 99%

Flight-Test Evaluation of Kinematic Precise Point Positioning of Small UAVs

Gross¹,

Watson²,

D'Urso³

et al. 2016

International Journal of Aerospace Engineering

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An experimental analysis of Global Positioning System (GPS) flight data collected onboard a Small Unmanned Aerial Vehicle (SUAV) is conducted in order to demonstrate that postprocessed kinematic Precise Point Positioning (PPP) solutions with precisions approximately 6 cm 3D Residual Sum of Squares (RSOS) can be obtained on SUAVs that have short duration flights with limited observational periods (i.e., only ~≤5 minutes of data). This is a significant result for the UAV flight testing community because an important and relevant benefit of the PPP technique over traditional Differential GPS (DGPS) techniques, such as Real-Time Kinematic (RTK), is that there is no requirement for maintaining a short baseline separation to a differential GNSS reference station. Because SUAVs are an attractive platform for applications such as aerial surveying, precision agriculture, and remote sensing, this paper offers an experimental evaluation of kinematic PPP estimation strategies using SUAV platform data. In particular, an analysis is presented in which the position solutions that are obtained from postprocessing recorded UAV flight data with various PPP software and strategies are compared to solutions that were obtained using traditional double-differenced ambiguity fixed carrier-phase Differential GPS (CP-DGPS). This offers valuable insight to assist designers of SUAV navigation systems whose applications require precise positioning.

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“…Finally, for the GPS-based heading and pitch estimates, 3°of uncertainty is assumed. Similar to the process-noise covariance matrix, these values make up a diagonal measurement-error covariance matrix, as shown in Equation (17).…”

Section: Nonlinear Kalman Filtermentioning

confidence: 99%

“…The experimental UAV used for this study is West Virginia University's (WVU's) Red Phastball Platform as shown in Figure 1, The Red Phastball is primarily used for sensor fusion research [17][18][19] and its avionics package [20] was updated for this study to include a Novatel OEM-615® dual-frequency GPS/Globalnaya Navigazionnaya Sputnikovaya Sistema (GLONASS) receiver, in which GPS pseudorange, carrier-phase and signal strength measurements were recorded at a rate of 10 Hz. In addition, for use in signal strength calibration and as pitch and roll reference solutions, the Red Phastball flew a Goodrich VG34® mechanical vertical gyroscope and the analog pitch and roll measurements were recorded using a micro-controller with a sampling rate of 50 Hz.…”

Section: Experimental Set-upmentioning

confidence: 99%

Fixed-Wing UAV Attitude Estimation Using Single Antenna GPS Signal Strength Measurements

Gross

Rhudy

2016

Aerospace

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This article considers a novel approach to using global positioning system (GPS) signal strength readings and estimated velocity vector for estimating the attitude of a small fixed-wing unmanned aerial vehicle (UAV). This approach has the benefit being able to estimate full position, velocity and attitude states of a UAV using only the data from a single GPS receiver and antenna. Two different approaches for utilizing GPS signal strength within measurement updates for UAV attitude in a nonlinear Kalman filter are discussed and assessed using recorded UAV flight data. Comparisons of UAV pitch and roll estimates against measurements from a high-grade mechanical gyroscope are used to show that approximately 5°error with respect to both mean and standard-deviation on both axes is achievable.

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Unmanned Aerial Vehicle Navigation Using Wide-Field Optical Flow and Inertial Sensors

Cited by 16 publications

References 32 publications

Reducing the complexity of visual navigation: Optical track controller for long‐range unmanned aerial vehicles

Reducing the complexity of visual navigation: Optical track controller for long‐range unmanned aerial vehicles

Flight-Test Evaluation of Kinematic Precise Point Positioning of Small UAVs

Fixed-Wing UAV Attitude Estimation Using Single Antenna GPS Signal Strength Measurements

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