A survey on vision-based UAV navigation

Lu, Yuncheng; Xue, Zhucun; Xia, Gui-Song; Zhang, Liangpei

doi:10.1080/10095020.2017.1420509

Cited by 304 publications

(149 citation statements)

References 63 publications

(62 reference statements)

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“…Most of the developed approaches have been conceived for autonomous navigation or collision avoidance instead of mapping tasks [28,29]. When the reconstruction of the 3D space needs to be accurate like in UAV applications [30], then the use of bundle adjustments (BA) is adopted to reduce the deformations of large blocks [31]. Unfortunately, BA is a time-consuming procedure especially for large blocks.…”

Section: Real-time Mappingmentioning

confidence: 99%

Towards Real-Time Building Damage Mapping with Low-Cost UAV Solutions

et al. 2019

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The timely and efficient generation of detailed damage maps is of fundamental importance following disaster events to speed up first responders’ (FR) rescue activities and help trapped victims. Several works dealing with the automated detection of building damages have been published in the last decade. The increasingly widespread availability of inexpensive UAV platforms has also driven their recent adoption for rescue operations (i.e., search and rescue). Their deployment, however, remains largely limited to visual image inspection by skilled operators, limiting their applicability in time-constrained real conditions. This paper proposes a new solution to autonomously map building damages with a commercial UAV in near real-time. The solution integrates different components that allow the live streaming of the images on a laptop and their processing on the fly. Advanced photogrammetric techniques and deep learning algorithms are combined to deliver a true-orthophoto showing the position of building damages, which are already processed by the time the UAV returns to base. These algorithms have been customized to deliver fast results, fulfilling the near real-time requirements. The complete solution has been tested in different conditions, and received positive feedback by the FR involved in the EU funded project INACHUS. Two realistic pilot tests are described in the paper. The achieved results show the great potential of the presented approach, how close the proposed solution is to FR’ expectations, and where more work is still needed.

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Section: Real-time Mappingmentioning

confidence: 99%

Towards Real-Time Building Damage Mapping with Low-Cost UAV Solutions

et al. 2019

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“…Visual object tracking plays an important role for the unmanned aerial vehicle (UAV). In literature, it has been widely used in different types of UAV applications, such as person following [1], automobile chasing [2], see-and-avoid [3], infrastructure inspection [4], wildlife monitoring [5], autonomous landing [6], aerial manipulation [7], and air-to-air refuel [8]. Although a growing number of visual tracking approaches have been designed for the UAV recently [9][10][11][12][13][14][15][16][17], visual tracking is still a challenging issue because of object appearance changes, which are generated by object deformation, illumination variation, scale changes, partial or full occlusion, blur motion, fast motion, in-plane or out-of-plane rotation, low image resolution, and cluttered background.…”

Section: Introductionmentioning

confidence: 99%

Online Semantic Subspace Learning with Siamese Network for UAV Tracking

Zha

Qiu

et al. 2020

Remote Sensing

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In urban environment monitoring, visual tracking on unmanned aerial vehicles (UAVs) can produce more applications owing to the inherent advantages, but it also brings new challenges for existing visual tracking approaches (such as complex background clutters, rotation, fast motion, small objects, and realtime issues due to camera motion and viewpoint changes). Based on the Siamese network, tracking can be conducted efficiently in recent UAV datasets. Unfortunately, the learned convolutional neural network (CNN) features are not discriminative when identifying the target from the background/clutter, In particular for the distractor, and cannot capture the appearance variations temporally. Additionally, occlusion and disappearance are also reasons for tracking failure. In this paper, a semantic subspace module is designed to be integrated into the Siamese network tracker to encode the local fine-grained details of the target for UAV tracking. More specifically, the target’s semantic subspace is learned online to adapt to the target in the temporal domain. Additionally, the pixel-wise response of the semantic subspace can be used to detect occlusion and disappearance of the target, and this enables reasonable updating to relieve model drifting. Substantial experiments conducted on challenging UAV benchmarks illustrate that the proposed method can obtain competitive results in both accuracy and efficiency when they are applied to UAV videos.

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“…However, the methods mentioned above have problems such as integral and cumulative error, signal loss and the possibility to be interfered by continuously changing electromagnetic signal. With the advantages of low cost, rich navigation information, and strong anti-electromagnetic interference capability, vision navigation has undergone a great development in recent years and can be a good alternative to the common navigation system [14,15].…”

Section: Introductionmentioning

confidence: 99%

An Improved Optical Flow Algorithm Based on Mask-R-CNN and K-Means for Velocity Calculation

et al. 2019

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Aiming at enhancing the accuracy and reliability of velocity calculation in vision navigation, an improved method is proposed in this paper. The method integrates Mask-R-CNN (Mask Region-based Convolutional Neural Network) and K-Means with the pyramid Lucas Kanade algorithm in order to reduce the harmful effect of moving objects on velocity calculation. Firstly, Mask-R-CNN is used to recognize the objects which have motions relative to the ground and covers them with masks to enhance the similarity between pixels and to reduce the impacts of the noisy moving pixels. Then, the pyramid Lucas Kanade algorithm is used to calculate the optical flow value. Finally, the value is clustered by the K-Means algorithm to abandon the outliers, and vehicle velocity is calculated by the processed optical flow. The prominent advantages of the proposed algorithm are (i) decreasing the bad impacts to velocity calculation, due to the objects which have relative motions; (ii) obtaining the correct optical flow sets and velocity calculation outputs with less fluctuation; and (iii) the applicability enhancement of the optical flow algorithm in complex navigation environment. The proposed algorithm is tested by actual experiments. Results with superior precision and reliability show the feasibility and effectiveness of the proposed method for vehicle velocity calculation in vision navigation system.

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A survey on vision-based UAV navigation

Cited by 304 publications

References 63 publications

Towards Real-Time Building Damage Mapping with Low-Cost UAV Solutions

Towards Real-Time Building Damage Mapping with Low-Cost UAV Solutions

Online Semantic Subspace Learning with Siamese Network for UAV Tracking

An Improved Optical Flow Algorithm Based on Mask-R-CNN and K-Means for Velocity Calculation

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