A New On-Board UAV Pose Estimation System Based on Monocular Camera

Xie, Yuan; Pan, Feng; Xing, Boyang; Gao, Qi; Feng, Xiaoxue; Li, Weixing

doi:10.1109/ihmsc.2016.180

Cited by 5 publications

(5 citation statements)

References 14 publications

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“…[11] and Xie et al. [12] proposed a system for unmanned aerial vehicle (UAV) 6DOF pose estimation. Su et al.…”

Section: Related Workmentioning

confidence: 99%

“…Camera technologies such as infrared and deep cameras were introduced to contribute to the pose estimation approaches. Su et al [11] and Xie et al [12] proposed a system for unmanned aerial vehicle (UAV) 6DOF pose estimation. Su et al [11] used an infrared active marker and a colored passive marker to mark the relative pose of the target.…”

Section: Related Workmentioning

confidence: 99%

“…With two different types of sensors, a particle swarm optimization (PSO) algorithm was employed to solve the optimal pose of a UAV. Xie et al [12] also used a PSO for the correspondence search between landmarks and image detections, and then applied the Kalman filter to track and predict pose of the UAV in space.…”

Section: Related Workmentioning

confidence: 99%

“…Xie et al. [12] also used a PSO for the correspondence search between landmarks and image detections, and then applied the Kalman filter to track and predict pose of the UAV in space.…”

Section: Related Workmentioning

confidence: 99%

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A single camera 360‐degree real time vision‐based localization method with application to mobile robot trajectory tracking

Singhirunnusorn

Fahimi

Aygün

2021

IET Cyber-Syst and Robotics

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A method is proposed for real-time vision-based localization in the 360°area around a three-dimensional (3D) reference object with a single camera. The problem is split into three subproblems. First, 360°3D object recognition is proposed, in which a computer vision solution can recognize a reference object from all possible 360°locations. Second, 360°pose estimation is presented, in which the pose of a robot at all locations is estimated. Third, a 360°localization application is integrated with a closed-loop real-time trajectory tracking controller. The successful results of simulations and real experiments of trajectory tracking are also presented.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…[11] and Xie et al. [12] proposed a system for unmanned aerial vehicle (UAV) 6DOF pose estimation. Su et al.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…Xie et al. [12] also used a PSO for the correspondence search between landmarks and image detections, and then applied the Kalman filter to track and predict pose of the UAV in space.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

A single camera 360‐degree real time vision‐based localization method with application to mobile robot trajectory tracking

Singhirunnusorn

Fahimi

Aygün

2021

IET Cyber-Syst and Robotics

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“…In the experiment, they randomly generated six points on the same plane of simulation environment, and then verified the accuracy of PnP algorithm. The method proposed by Xie et al [11] required more than three special markers to estimate the UAV’s pose, and these markers could be placed arbitrarily. Fu et al [12] proposed an off-board quadrotor pose estimation method.…”

Section: Introductionmentioning

confidence: 99%

Drone Detection and Pose Estimation Using Relational Graph Networks

Ren

Jiang

et al. 2019

Sensors

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With the upsurge in use of Unmanned Aerial Vehicles (UAVs), drone detection and pose estimation by using optical sensors becomes an important research subject in cooperative flight and low-altitude security. The existing technology only obtains the position of the target UAV based on object detection methods. To achieve better adaptability and enhanced cooperative performance, the attitude information of the target drone becomes a key message to understand its state and intention, e.g., the acceleration of quadrotors. At present, most of the object 6D pose estimation algorithms depend on accurate pose annotation or a 3D target model, which costs a lot of human resource and is difficult to apply to non-cooperative targets. To overcome these problems, a quadrotor 6D pose estimation algorithm was proposed in this paper. It was based on keypoints detection (only need keypoints annotation), relational graph network and perspective-n-point (PnP) algorithm, which achieves state-of-the-art performance both in simulation and real scenario. In addition, the inference ability of our relational graph network to the keypoints of four motors was also evaluated. The accuracy and speed were improved significantly compared with the state-of-the-art keypoints detection algorithm.

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