A Hierarchical Vision-Based UAV Localization for an Open Landing

Yuan, Haiwen; Xiao, Changshi; Xiu, Supu; Zhan, Wenqiang; Ye, Zhenyi; Zhang, Fan; Zhou, Chunhui; Wen, Yuanqiao; Li, Qiliang

doi:10.3390/electronics7050068

Cited by 16 publications

(13 citation statements)

References 26 publications

(29 reference statements)

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“…Kyristsis [5] introduced a hierarchical vision-based localization framework for rotor UAV, which is proposed for an open landing, and a federated Extended Kalman Filter (EKF) structure was costumed to estimate the pose of the vehicle.…”

Section: A Existing Algorithmsmentioning

confidence: 99%

“…(1) Nguyen [3] (2) Yuan [5] (3) Wubben [7] (4) Liu [8] (5) Yang [9] (6) Lin [17] (7) Our landing marker [42][43] (8) control points in our landing marker [42][43]…”

Section: B Analysis Of the Existing Algorithmsmentioning

confidence: 99%

“…We will elaborate on these related technologies in the "Related work" section. In the relevant state-of-the-art literature that we have collected, most of them [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] studied the landing technology of rotorcraft UAVs, and only [1][2] investigated the technology related to the fixed-wing UAV landing. However, Kong [1] stated that his method relies on the ground-based stereo guidance system, and Zhang [2] focused on a method for high-precision altitude estimation.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

High Speed Safe Autonomous Landing Marker Tracking of Fixed Wing Drone Based on Deep Learning

Yuan

Wang

2022

IEEE Access

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Most of the current research on autonomous landing of Unmanned Aerial Vehicles (UAVs) focus on rotorcrafts, which can fly horizontally over the landing site and then land vertically, with less landing risk. However, some of the unmanned fixed-wing aircrafts take off and land in the sliding mode of wheel landing gears, and their landing stage is difficult to control and easier to failure. In this paper, the autonomous landing of the fixed wing UAVs based on the visual navigation is studied. We propose a deep learning based Vision Transformer Particle Region-based Convolutional Neural Network (VitP-RCNN). Our VitP-RCNN uses Mobile Vision Transformers (MobileViT) as the backbone to accelerate feature extraction. Regarding the innovation of the present study, it is noticed that candidate boxes are chosen as the particles in our methodology, where the confidence is defined as the particle weight, the spatio-temopral correlation between adjacent image is used in video tracking and the Particle Filter theory is employed into the two-stage detection network to construct the present VitP-RCNN. Based on the spatial-temporal correlation, Our VitP-RCNN is efficient in predicting the particle state in the next frame through the state of the previous frame, so as to realize the continuous tracking of the landing marker. The experimental results show that on the Jetson AGX Xavier drone platform, our speed is up to 51FPS, enabling us to perform the landing marker tracking for the autonomous landing of the fixed-wing drones.INDEX TERMS Unmanned Aerial Vehicle (UAV); drone; autonomous landing; target detection; target tracking; deep learning

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Section: A Existing Algorithmsmentioning

confidence: 99%

“…(1) Nguyen [3] (2) Yuan [5] (3) Wubben [7] (4) Liu [8] (5) Yang [9] (6) Lin [17] (7) Our landing marker [42][43] (8) control points in our landing marker [42][43]…”

Section: B Analysis Of the Existing Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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High Speed Safe Autonomous Landing Marker Tracking of Fixed Wing Drone Based on Deep Learning

Yuan

Wang

2022

IEEE Access

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“…Then, RANSAC is used to stitch the fine line segments to detect the whole horizon line. To solve this problem, some researchers attempt to utilize the unmanned aerial vehicle (UAV) to enhance the visual perception ability of the USV [25,26], which increases the complexity of the unmanned system.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Semantic Segmentation for Unmanned Surface Vehicle Navigation

et al. 2020

Self Cite

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The intelligentization of unmanned surface vehicles (USVs) has recently attracted intensive interest. Visual perception of the water scenes is critical for the autonomous navigation of USVs. In this paper, an adaptive semantic segmentation method is proposed to recognize the water scenes. A semantic segmentation network model is designed to classify each pixel of an image into water, land or sky. The segmentation result is refined by the conditional random field (CRF) method. It is further improved accordingly by referring to the superpixel map. A weight map is generated based on the prediction confidence. The network trains itself with the refined pseudo label and the weight map. A set of experiments were designed to evaluate the proposed method. The experimental results show that the proposed method exhibits excellent performance with few-shot learning and is quite adaptable to a new environment, very efficient for limited manual labeled data utilization.

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“…A new growing interest is the integration of nanomaterials and devices into smart systems for stand-alone applications. For example, a robotic vessel equipped with vision sensors [19] and a smart nanoelectronics sensor system governed by machine-learning intelligence will be of great interest to the academic society and industry.…”

Section: Future Trendsmentioning

confidence: 99%