UAV Navigation System Autonomous Correction Algorithm Based on Road and River Network Recognition

Tanchenko, A. P.; Fedulin, A. M.; Bikmaev, Ramil; Sadekov, R. N.

doi:10.1134/s2075108720040100

Cited by 7 publications

(3 citation statements)

References 20 publications

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“…The main comparison is the homography between the visual image system segmented by a computer using CNN and the vector map image. The mathematical and flight experimental results show that the algorithm is effective in navigation applications [ 1 ]. Satapathy and colleagues propose a new deep learning model.…”

Section: Introductionmentioning

confidence: 99%

Machine Vision and Intelligent Algorithm Based on Neural Network

Sun

2022

Computational Intelligence and Neuroscience

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Neural network algorithms and intelligent algorithms are hot topics in the field of deep learning. In this study, the neural network algorithm and intelligence are optimized, and it is used in simulation experiments to improve the target image recognition ability of the algorithm in the machine vision environment. First, this paper introduces the application of neural networks in the field of machine vision. Second, in the experiment, the improved VGG-16 convolutional neural network (CNN) model is applied to metal block defect detection. Experimental results show that the optimized network can classify metal block defects with the maximum accuracy of 99.28%. Then, the intelligent algorithm based on neural network is studied, and the CIFAR-10 data set is taken as the experimental target for training test and verification test. Using BP algorithm, particle swarm optimization algorithm (PSO-BP), and improved neural network algorithm, respectively, the convergence speed of ICS algorithm based on BP neural network is compared. In contrast, ICS-BP algorithm has the fastest convergence speed and converges when the number of iterations is 32, followed by PSO-BP algorithm.

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Section: Introductionmentioning

confidence: 99%

Machine Vision and Intelligent Algorithm Based on Neural Network

Sun

2022

Computational Intelligence and Neuroscience

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“…With a priori information in navigation problems, an autonomous correction algorithm can be used to compare visual maps with topographic maps. Tanchenko et al (2020) integrate prior information by computing the least squares of homography between two maps, one of which is from machine vision and the other from prior vector maps. Its effectiveness is verified by mathematics calculation and flight experiments.…”

Section: Drone Localization In Underground Structuresmentioning

confidence: 99%

“…To this end, some researchers improved the optical flow algorithm by brightness compensation of adjacent frames(Lin et al, 2021). It adopts Shi-Tomasi (ST) detector to make up for the poor resistance of the optical flow algorithm to illumination changes.With a priori information in navigation problems, an autonomous correction algorithm can be used to compare visual maps with topographic maps Tanchenko et al (2020). integrate prior information by computing the least squares of homography between two maps, one of which is from machine vision and the other from prior vector maps.Its effectiveness is verified by mathematics calculation and flight experiments.…”

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confidence: 99%

Unmanned aerial vehicle navigation in underground structure inspection: A review

Zhang,

Hao,

Zhang

et al. 2023

Geological Journal

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Many years after construction, a number of existing old tunnels and underground structures are deteriorating with time as evidenced by cracks, large deformations, water leakage and so forth, which usually require regular site inspections to record their structural deterioration by taking high‐pixel, high‐overlap images along miles of a tunnel network. For complex underground structures (e.g., long tunnels and large caves), unmanned aerial vehicles (UAVs) may be adaptive in acquiring images at multiple heights and angles with low operational costs. So far, UAV underground structural health monitoring has become mature for open‐air surveying with rapid developments in robotic software and hardware. However, the UAV image acquisition for underground working conditions still faces a number of key challenges. This paper aims to provide an overview of UAV navigation techniques in confined dark spaces for geotechnical engineers, geologists, drone developers and other interdisciplinary researchers & professionals in the structural health monitoring field. It specifies the challenges for UAV application in underground space, mainly including lack of Global Navigation Satellite System (GNSS) signals, poor lighting conditions, weak features and obstacle avoidance and then followed by strategic solutions. For example, in light of poor GNSS signals, the fusion of multi‐sensors (e.g., laser imaging, detection and ranging (LiDAR) and multi‐cameras) can enhance localization accuracy in low‐luminance underground conditions. To address obstacle avoidance, computer vision (CV)‐based navigation algorithms (e.g., deep reinforced learning [DRL]) enable effective navigation in complex 3D spaces, but their adaptability is limited by arithmetic power and pre‐training needs. The review of relevant previous studies concludes that further development for UAVs in underground space inspection may focus on operation in large‐scale geometric inspection environments, obstacle avoidance, features and semantic recognition.

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