Inverse optical flow based guidance for UAV navigation through urban canyons

Agrawal, Pooja; Ratnoo, Ashwini; Ghose, Debasish

doi:10.1016/j.ast.2017.05.012

Cited by 29 publications

(10 citation statements)

References 14 publications

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“…where σ is the radius scaling factor and σ = 1 indicates that the optimal observation area circumscribes the target image. From Equations (10) and (11), the desired flying height of the quadrotor can be written as…”

Section: Relative Distance Estimation In Vertical Directionmentioning

confidence: 99%

“…However, GPS cannot determine the relative position between the UAV and a target, unless the target's position information is accurately known. Moreover, the accuracy of GPS is unreliable and ineffective in the presence of obstacles or external disturbances, such as urban canyon effects or electromagnetic interference [10,11]. On the other hand, a large number of UAVs, especially small-scale quadrotors, are equipped with cameras which can provide adequate visual information and act as an alternative option to GPS.…”

Section: Introductionmentioning

confidence: 99%

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Robust Vision-Based Control of a Rotorcraft UAV for Uncooperative Target Tracking

Zhang

Zhao

Zhou

2020

Sensors

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This paper investigates the problem of using an unmanned aerial vehicle (UAV) to track and hover above an uncooperative target, such as an unvisited area or an object that is newly discovered. A vision-based strategy integrating the metrology and the control is employed to achieve target tracking and hovering observation. First, by introducing a virtual camera frame, the reprojected image features can change independently of the rotational motion of the vehicle. The image centroid and an optimal observation area on the virtual image plane are exploited to regulate the relative horizontal and vertical distance. Then, the optic flow and gyro measurements are utilized to estimate the relative UAV-to-target velocity. Further, a gain-switching proportional-derivative (PD) control scheme is proposed to compensate for the external interference and model uncertainties. The closed-loop system is proven to be exponentially stable, based on the Lyapunov method. Finally, simulation results are presented to demonstrate the effectiveness of the proposed vision-based strategy in both hovering and tracking scenarios.

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Section: Relative Distance Estimation In Vertical Directionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Vision-Based Control of a Rotorcraft UAV for Uncooperative Target Tracking

Zhang

Zhao

Zhou

2020

Sensors

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“…At the sampling interval of the OF data, where there are only SIFT data and no OF data, there is no way to obtain the residual error directly. However, according to (15), in the optical flow sampling interval where covariance matrix R t → ∞ and k t → 0. Therefore, only the filter parameters in the time update are meaningful and (15) can be modified to read:…”

Section: B Multi-rate Data Fusion Algorithm Based On Residual Correcmentioning

confidence: 99%

“…For example, [14] proposed an information fusion method based on a microelectromechanical systems-based inertial measurement unit (MEMS-IMU) and OF, which was used to correct the MEMS-IMU's attitude when it diverged; simulation results showed that modification of the vehicle attitude in combination with OF provided good performance, with the advantages of smaller errors, slow divergence and improved robustness. In [15], a vision-based UAV navigation method for use in urban and canyon environments was proposed; UAV navigation experiments in linear, L-shaped and T-shaped canyons were completed using OF as calculated from image sequences taken by the airborne camera. In [16], OF is used as a velocity measurement method to estimate the forward velocity of a quadrotor UAV.…”

Section: Introductionmentioning

confidence: 99%

Integrated Velocity Measurement Algorithm Based on Optical Flow and Scale-Invariant Feature Transform

et al. 2019

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The pyramid Lucas-Kanade (LK) optical flow algorithm has been widely used in velocity measurement applications. However, these applications are limited by some shortcomings of the algorithm, such as its slow calculation speed and susceptibility to illumination changes. To solve these problems, a data fusion scheme based on the scale-invariant feature transform (SIFT) and optical flow is proposed to alleviate the dependence of the optical flow on the illumination conditions. In addition, an improved cubature Kalman filter (CKF) based on multi-rate residual correction (CKF-MRC) is proposed to solve the problem of inconsistency between the sampling frequencies of the SIFT and the optical flow, and takes full advantage of the high sampling frequency of SIFT. The experimental results demonstrate that the proposed CKF-MRC method can effectively improve the accuracy of velocity measurement under variable illumination conditions with a high sampling frequency. INDEX TERMS Cubature Kalman filter, optical flow, residual error correction, scale-invariant feature transform.

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“…Obstacle detection is only one of the goals; others include the accurate avoidance of a ground obstacle or flights in a complex environment [ 20 ] and require the implementation of different methods or algorithms to attain them. If, for example, the ground obstacles are of various sizes and shapes or have a different height, different mapping techniques should be applied, as demonstrated in [ 21 ] and, where ellipsoidal geometry is used, in [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

Adaptation of Dubins Paths for UAV Ground Obstacle Avoidance When Using a Low Cost On-Board GNSS Sensor

Kikutis

Stankūnas

Rudinskas

et al. 2017

Sensors

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Current research on Unmanned Aerial Vehicles (UAVs) shows a lot of interest in autonomous UAV navigation. This interest is mainly driven by the necessity to meet the rules and restrictions for small UAV flights that are issued by various international and national legal organizations. In order to lower these restrictions, new levels of automation and flight safety must be reached. In this paper, a new method for ground obstacle avoidance derived by using UAV navigation based on the Dubins paths algorithm is presented. The accuracy of the proposed method has been tested, and research results have been obtained by using Software-in-the-Loop (SITL) simulation and real UAV flights, with the measurements done with a low cost Global Navigation Satellite System (GNSS) sensor. All tests were carried out in a three-dimensional space, but the height accuracy was not assessed. The GNSS navigation data for the ground obstacle avoidance algorithm is evaluated statistically.

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Inverse optical flow based guidance for UAV navigation through urban canyons

Cited by 29 publications

References 14 publications

Robust Vision-Based Control of a Rotorcraft UAV for Uncooperative Target Tracking

Robust Vision-Based Control of a Rotorcraft UAV for Uncooperative Target Tracking

Integrated Velocity Measurement Algorithm Based on Optical Flow and Scale-Invariant Feature Transform

Adaptation of Dubins Paths for UAV Ground Obstacle Avoidance When Using a Low Cost On-Board GNSS Sensor

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