Autonomous target following with monocular camera on UAS using Recursive-RANSAC tracker

Lee, Jae H.; Millard, Jeff D.; Lusk, Parker C.; Beard, Randal W.

doi:10.1109/icuas.2018.8453285

Cited by 7 publications

(4 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A UAS has been used to visually estimate the pose of a person in seven studies estimating position only on short distances (Lim and Sinha, 2015 ; Bian et al, 2016 ; Mendonça et al, 2016 ; Monajjemi et al, 2016 ; Cheng et al, 2017 ; Lee et al, 2018 ; Liu et al, 2018 ). In those studies, only the position was estimated (not orientation) and the person was close to the UAS.…”

Section: Related Workmentioning

confidence: 99%

Visual Pose Estimation of Rescue Unmanned Surface Vehicle From Unmanned Aerial System

Dufek

Murphy

2019

Front. Robot. AI

View full text Add to dashboard Cite

This article addresses the problem of how to visually estimate the pose of a rescue unmanned surface vehicle (USV) using an unmanned aerial system (UAS) in marine mass casualty events. A UAS visually navigating the USV can help solve problems with teleoperation and manpower requirements. The solution has to estimate full pose (both position and orientation) and has to work in an outdoor environment from oblique view angle (up to 85 • from nadir) at large distances (180 m) in real-time (5 Hz) and assume both moving UAS (up to 22 m s −1) and moving object (up to 10 m s −1). None of the 58 reviewed studies satisfied all those requirements. This article presents two algorithms for visual position estimation using the object's hue (thresholding and histogramming) and four techniques for visual orientation estimation using the object's shape while satisfying those requirements. Four physical experiments were performed to validate the feasibility and compare the thresholding and histogramming algorithms. The histogramming had statistically significantly lower position estimation error compared to thresholding for all four trials (p-value ranged from ∼ 0 to 8.23263 × 10 −29), but it only had statistically significantly lower orientation estimation error for two of the trials (p-values 3.51852 × 10 −39 and 1.32762 × 10 −46). The mean position estimation error ranged from 7 to 43 px while the mean orientation estimation error ranged from 0.134 to 0.480 rad. The histogramming algorithm demonstrated feasibility for variations in environmental conditions and physical settings while requiring fewer parameters than thresholding. However, three problems were identified. The orientation estimation error was quite large for both algorithms, both algorithms required manual tuning before each trial, and both algorithms were not robust enough to recover from significant changes in illumination conditions. To reduce the orientation estimation error, inverse perspective warping will be necessary to reduce the perspective distortion. To eliminate the necessity for tuning and increase the robustness, a machine learning approach to pose estimation might ultimately be a better solution.

show abstract

Section: Related Workmentioning

confidence: 99%

Visual Pose Estimation of Rescue Unmanned Surface Vehicle From Unmanned Aerial System

Dufek

Murphy

2019

Front. Robot. AI

View full text Add to dashboard Cite

show abstract

“…Model-based optimization was applied to achieve perception and action objectives for robust sensing of a UAV [ 22 ]; however, the image features were extracted for indoor localization, rather than target tracking. Bounding boxes and feature points have been utilized as image features for tracking human beings [ 34 , 35 , 36 , 37 ], but the targets were moving at speeds that were so low that their motions could be ignored during tracking. Moreover, the orientation of the target is unavailable [ 38 ].…”

Section: Introductionmentioning

confidence: 99%

“…The contributions of this work can be summarized as follows: An optimal tracking controller is developed to track a moving target undergoing unknown motion while meeting motion, control, and sensing constraints, where the relative motion between the target and the UAV is estimated. In contrast to previous approaches [ 34 , 35 , 36 , 37 , 38 ], this work models the dynamics of the relative rotation between the target and the UAV, and the relative angle can be controlled to a predefined desired value, which can be applied in applications such as the automatic searching, detection, and recognition of car license plate. The controller is designed to ensure that the target remains within the FOV.…”

Section: Introductionmentioning

confidence: 99%

“…In contrast to previous approaches [ 34 , 35 , 36 , 37 , 38 ], this work models the dynamics of the relative rotation between the target and the UAV, and the relative angle can be controlled to a predefined desired value, which can be applied in applications such as the automatic searching, detection, and recognition of car license plate.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Visual Servoing of a Moving Target by an Unmanned Aerial Vehicle

Chen

Hung

Yang

et al. 2021

Sensors

View full text Add to dashboard Cite

To track moving targets undergoing unknown translational and rotational motions, a tracking controller is developed for unmanned aerial vehicles (UAVs). The main challenges are to control both the relative position and orientation between the target and the UAV to within desired values, and to guarantee that the generated control input to the UAV is feasible (i.e., below its motion capability). Moreover, the UAV is controlled to ensure that the target always remains within the field of view of the onboard camera. These control objectives were achieved by developing a nonlinear-model predictive controller, in which the future motion of the target is predicted by quadratic programming (QP). Since constraints of the feature vector and the control input are considered when solving the optimal control problem, the control inputs can be bounded and the target can remain inside the image. Three simulations were performed to compare the efficacy and performance of the developed controller with a traditional image-based visual servoing controller.

show abstract

UAV target following in complex occluded environments with adaptive multi-modal fusion

Wang

Cai

et al. 2022

Appl Intell

View full text Add to dashboard Cite

Autonomous target following with monocular camera on UAS using Recursive-RANSAC tracker

Cited by 7 publications

References 12 publications

Visual Pose Estimation of Rescue Unmanned Surface Vehicle From Unmanned Aerial System

Visual Pose Estimation of Rescue Unmanned Surface Vehicle From Unmanned Aerial System

Visual Servoing of a Moving Target by an Unmanned Aerial Vehicle

UAV target following in complex occluded environments with adaptive multi-modal fusion

Contact Info

Product

Resources

About