Map2DFusion: Real-time incremental UAV image mosaicing based on monocular SLAM

Bu, Shuhui; Zhao, Yong; Wan, Gang; Liu, Zhenbao

doi:10.1109/iros.2016.7759672

Cited by 62 publications

(40 citation statements)

References 29 publications

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“…Reconstrucción de superficies: una vez que las imágenes están orientadas y se conoce su posición relativa, es posible derivar de ellas una nube de puntos 3D. En [19], [20] se presentan programas capaces de transmitir en vivo imágenes del dron, fundirlas y mostrarlas al usuario como un mapa que se va expandiendo y actualizando conforme el dron adquiere nuevas imágenes. Este proceso también puede llevarse a cabo después de la misión, con diferentes programas tales como MICMAC, evaluado en [21] o SFMToolkit, usado en [12].…”

Section: B Post-procesamiento De Datosunclassified

“…Con respecto a los drones usados, hay dos tipos que se usan con preferencia: los drones de ala fija (similares a aviones), usados por ejemplo en [1], [23], [20]; y los drones de ala rotatoria (similares a helicópteros), usados en [24], [9], [19], [25], [21].…”

Section: B Post-procesamiento De Datosunclassified

“…Con respecto a los paquetes de software, existen paquetes comerciales y gratuitos. Ejemplos de los primeros son Agisoft PhotoScan, usado en [1], [9], [19], y Pix4D, usado en [25], [9], [19], así como 3DSurvey y SURE, evaluados en [9]. Algunos de los paquetes gratuitos y de código abierto son QGIS; Map2DFusion, presentado en [19]; SFMToolkit3, usado en [12]; OpenDroneMap, usado en [23]; MICMAC, presentado en [21]; y APERO, del mismo equipo, presentado en [13].…”

Section: B Post-procesamiento De Datosunclassified

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Uso de software de código abierto para fusión de imágenes agrícolas multiespectrales adquiridas con drones

Reyes¹,

Godoy²,

Realpe³

2019

Proceedings of the 17th LACCEI International Multi-Conference for Engineering, Education, and Technology: “Industry, Innovation

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Drones or unmanned aerial vehicle (UAV) are very useful for image acquisition. However, all the images acquired during a flight must be combined in order to create valuable information about a terrain or crop. There are many programs that combine images (image registration), each one with different properties (performance, precision, price, etc.). In this project some Open Source programs were analyzed in order to define the best software for Ecuadorian small and medium-sized farmers. The results obtained could be of interest for software developers since they may modify and integrate the programs into a simpler workflow. Additionally, it allows to reduce costs since no license payment are needed, which may motive small and medium-sized farmers. A public repository has been created as part of the results of this project, which includes code for pre-processing multispectral images and code for creating RGB, CIR and NDVI fused maps.

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Section: B Post-procesamiento De Datosunclassified

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Uso de software de código abierto para fusión de imágenes agrícolas multiespectrales adquiridas con drones

Reyes¹,

Godoy²,

Realpe³

2019

Proceedings of the 17th LACCEI International Multi-Conference for Engineering, Education, and Technology: “Industry, Innovation

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“…For example, researches to solve problems caused by using a large number of images, such as mosaicking acceleration, brightness correction, and seamline extraction methods were studied (Moussa and ElSheimy, 2016;Mehrdad et al, 2016;Li et al, 2012;Bu et al, 2016). However, they did not fully consider inherent characteristics of images from small UAV.…”

Section: Introductionmentioning

confidence: 99%

Development of a Robust Image Mosaicking Method for Small Unmanned Aerial Vehicle

Kim

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VI, WG VI/4KEY WORDS: UAV, Image Mosaicking, Imaging Geometry Characteristics, Near-ortho mosaic ABSTRACT:In this paper, a tie-point based image mosaicking method considering imaging characteristics of small UAVs is proposed. Small UAVs can be characterized to have unstable flight trajectory and lower flight height. The proposed method considers the imaging characteristics in image transformation estimation and image blending process. For image transformation estimation, an optimal transformation model is variably applied by using tie-point area ratio. The optimal tie-point area ratio was about 0.3. Mosaicking error was largely decreased by using this tie-point area ratio. For image blending, a composite area minimization is introduced as a preceding step of image resampling. Composite areas of individual images were minimized by analyzing image overlaps between adjacent images. The proposed method was evaluated over flat area and urban area with highly overlapping multi-strip and inconsistently overlapping strip. Experiment results showed that the proposed method can reliably generate mosaics not only from UAV images acquired in good environment but also from extreme environment.

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“…The experiment is performed on a computer with i7-6700 CPU, 16GB RAM running 64bit Ubuntu. 400 and 10k images from DroneMap[7] dataset are used to train the models with 4 and 6 levels.…”

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confidence: 99%

GSLAM: A General SLAM Framework and Benchmark

Zhao

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

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SLAM technology has recently seen many successes and attracted the attention of high-technological companies. However, how to unify the interface of existing or emerging algorithms, and effectively perform benchmark about the speed, robustness and portability are still problems. In this paper, we propose a novel SLAM platform named GSLAM, which not only provides evaluation functionality, but also supplies useful toolkit for researchers to quickly develop their own SLAM systems. The core contribution of GSLAM is an universal, cross-platform and full open-source SLAM interface for both research and commercial usage, which is aimed to handle interactions with input dataset, SLAM implementation, visualization and applications in an unified framework. Through this platform, users can implement their own functions for better performance with plugin form and further boost the application to practical usage of the SLAM. *

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Map2DFusion: Real-time incremental UAV image mosaicing based on monocular SLAM

Cited by 62 publications

References 29 publications

Uso de software de código abierto para fusión de imágenes agrícolas multiespectrales adquiridas con drones

Uso de software de código abierto para fusión de imágenes agrícolas multiespectrales adquiridas con drones

Development of a Robust Image Mosaicking Method for Small Unmanned Aerial Vehicle

GSLAM: A General SLAM Framework and Benchmark

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