Real-time, GPU-based pose estimation of a UAV for autonomous takeoff and landing

Benini, Alessandro; Rutherford, Matthew J.; Valavanis, Kimon P.

doi:10.1109/icra.2016.7487525

Cited by 35 publications

(15 citation statements)

References 17 publications

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“…Zhuang et al [12] used the line features of the airport and the monocular camera on board to provide pose information for UAV autonomous landing. Benini et al [13] estimated the pose of a UAV by detecting a marker composed of known circles for autonomous takeoff and landing. For scenes without known landmarks, the structure from motion (SFM) [14,15,16] method or the simultaneous localization and mapping (SLAM) [17,18] method can be leveraged to estimate the relative pose for aircraft navigation.…”

Section: Introductionmentioning

confidence: 99%

Pose Estimation for Straight Wing Aircraft Based on Consistent Line Clustering and Planes Intersection

Teng

Luo³

et al. 2019

Sensors

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Aircraft pose estimation is a necessary technology in aerospace applications, and accurate pose parameters are the foundation for many aerospace tasks. In this paper, we propose a novel pose estimation method for straight wing aircraft without relying on 3D models or other datasets, and two widely separated cameras are used to acquire the pose information. Because of the large baseline and long-distance imaging, feature point matching is difficult and inaccurate in this configuration. In our method, line features are extracted to describe the structure of straight wing aircraft in images, and pose estimation is performed based on the common geometry constraints of straight wing aircraft. The spatial and length consistency of the line features is used to exclude irrelevant line segments belonging to the background or other parts of the aircraft, and density-based parallel line clustering is utilized to extract the aircraft’s main structure. After identifying the orientation of the fuselage and wings in images, planes intersection is used to estimate the 3D localization and attitude of the aircraft. Experimental results show that our method estimates the aircraft pose accurately and robustly.

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Section: Introductionmentioning

confidence: 99%

Pose Estimation for Straight Wing Aircraft Based on Consistent Line Clustering and Planes Intersection

Teng

Luo³

et al. 2019

Sensors

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“…Known 3D geometry features in observed scenes are exploited and a perspective-and-point (PnP) algorithm is selected to estimate the pose accurately. Benini et al [44] proposed an aircraft pose estimation system based on the detection of a known marker. The on-board system performs high-frequency pose estimation for autonomous landing using parallel image processing.…”

Section: Introductionmentioning

confidence: 99%

Aircraft Pose Estimation Based on Geometry Structure Features and Line Correspondences

Teng

Luo

et al. 2019

Sensors

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A robust and accurate aircraft pose estimation method is proposed in this paper. The aircraft pose reflects the flight status of the aircraft and accurate pose measurement is of great importance in many aerospace applications. This work aims to establish a universal framework to estimate the aircraft pose based on generic geometry structure features. In our method, line features are extracted to describe the structure of an aircraft in single images and the generic geometry features are exploited to form line groups for aircraft structure recognition. Parallel line clustering is utilized to detect the fuselage reference line and bilateral symmetry property of aircraft provides an important constraint for the extraction of wing edge lines under weak perspective projection. After identifying the main structure of the aircraft, a planes intersection method is used to obtain the 3D pose parameters based on the established line correspondences. Our proposed method can increase the measuring range of binocular vision sensors and has the advantage of not relying on 3D models, cooperative marks or other feature datasets. Experimental results show that our method can obtain reliable and accurate pose information of different types of aircraft.

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“…Hence, the algorithm in [13] is limited to human pose estimation, applicable for lowresolution images and has longer execution time than our implementation. The algorithm described in [14] is used for highly accurate pose estimation in un-manned air vehicles (UAV) for takeoff and landings. The authors in [14] perform a field evaluation using low end GPUs such as Jetson TK1.…”

Section: Introductionmentioning

confidence: 99%

“…The algorithm described in [14] is used for highly accurate pose estimation in un-manned air vehicles (UAV) for takeoff and landings. The authors in [14] perform a field evaluation using low end GPUs such as Jetson TK1. It is observed that pose estimation takes under 25 milliseconds for standard 640 × 480 images.…”

Section: Introductionmentioning

confidence: 99%

Acceleration of Points to Convex Region Correspondence Pose Estimation Algorithm on GPUs for Real-Time Applications

Kumar¹,

Muknahallipatna²,

McInroy³

2016

JCC

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In our previous work, a novel algorithm to perform robust pose estimation was presented. The pose was estimated using points on the object to regions on image correspondence. The laboratory experiments conducted in the previous work showed that the accuracy of the estimated pose was over 99% for position and 84% for orientation estimations respectively. However, for larger objects, the algorithm requires a high number of points to achieve the same accuracy. The requirement of higher number of points makes the algorithm, computationally intensive resulting in the algorithm infeasible for real-time computer vision applications. In this paper, the algorithm is parallelized to run on NVIDIA GPUs. The results indicate that even for objects having more than 2000 points, the algorithm can estimate the pose in real time for each frame of high-resolution videos.

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Real-time, GPU-based pose estimation of a UAV for autonomous takeoff and landing

Cited by 35 publications

References 17 publications

Pose Estimation for Straight Wing Aircraft Based on Consistent Line Clustering and Planes Intersection

Pose Estimation for Straight Wing Aircraft Based on Consistent Line Clustering and Planes Intersection

Aircraft Pose Estimation Based on Geometry Structure Features and Line Correspondences

Acceleration of Points to Convex Region Correspondence Pose Estimation Algorithm on GPUs for Real-Time Applications

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