Aerial mapping using high resolution digital camera and unmanned aerial vehicle for Geographical Information System

Ahmad, Anuar; Samad, Abd Manan

doi:10.1109/cspa.2010.5545303

Cited by 27 publications

(14 citation statements)

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“…A noise factor is added corresponding to the observation error model, even when there is no motion. These calculations are represented in equation (Eq.1): (1) where , and correspond to the pose in world coordinates of the landmark , , and p correspond to the landmark pose in global coordinates based on the pose of particle , p and g are adjustable gains enabling to vary the weight sensitivity to errors in position and orientation, g g g g g g is the probability of the space corresponding to particle position to be free, where is the pair row / column corresponding to the position of the particle in the occupancy grid, and p is an independent factor representing the observation noise. After this procedure the weight of all the particles is then normalized so that the sum of all weights is one.…”

Section: A Global Localizationmentioning

confidence: 99%

“…Today there are several tasks already automated in different areas, such as the mapping and monitoring of land surface [1], large-scale agriculture [3], environmental monitoring [20] and industry [2]. On the other hand, there are high risk tasks or ones which are even impossible to accomplish by humans, such as the verification of explosive devices [5], the exploration and mapping of areas contaminated by biological or nuclear waste to assess the damage [15], or even space exploration of distant celestial bodies [13].…”

Section: Introductionmentioning

confidence: 99%

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Localization and navigation of a mobile robot in an office-like environment

Alves

Costelha

Neves

2013

2013 13th International Conference on Autonomous Robot Systems

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This article focuses on the localization and navigation of a mobile differential robot in an indoor officelike environment. These are fundamental issues to service robotics, which is a branch with a strong market growth. The work implements a vision tracking system, environment mapping, route planning and navigation for an autonomous robot application inside services buildings. One goal of the methodology is its application with low cost equipment. The test bed chosen was a Pioneer P3-DX robot [16] in a service building, with an attached USB webcam, pointed at the ceiling to take advantage of the position of the light fixtures as natural landmarks. The robot location is estimated through two distinct probabilistic methods: a particle filter, when there is no information about the starting location of the robot, and the Kalman filter, given the convergence of the particle filter. Both methods use the detection of light fixtures together with the robot kinematics as information to estimate the pose. The mapping of the environment and its obstacles is obtained from the localization estimates and the information gathered by ultrasound sensors, representing the entire navigation space discretized in the form of an occupation grid. Planning the navigation path is determined by a simple search algorithm, namely the Wavefront algorithm, based on the information contained in the occupancy grid. For a given path, navigation is performed with obstacle avoidance using the virtual forces method. Replanning is used to recover from local minima situations.

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Section: A Global Localizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Localization and navigation of a mobile robot in an office-like environment

Alves

Costelha

Neves

2013

2013 13th International Conference on Autonomous Robot Systems

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“…The results showed that the accuracy of the sub-meter is often obtained using the employed method. Topographic maps and ortho photos generated were used as an input to the Geographical Information System (GIS) for further image exploration [6]. Roberts -"GPS Guided Autonomous Drone.…”

Section: Literature Surveymentioning

confidence: 99%

“…If a multispectral camera is not available, the drone can still take overhead pictures of the fields and highlight areas where plants seem discolored or smaller than the rest of the field. This technique can also be used to detect underground leaks in existing irrigation systems [2] [6].…”

Section: Ease Of Use A) Monitoring Crop Productionmentioning

confidence: 99%

Design and Development of Agricultural Drone for Plant Health Analysis

Bharambe¹,

Singh²,

Binsale³

2020

IRJIET

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Agricultural drones let the farmers observe and analyze the fields/ sectors from the sky. This overview of the sector can reveal several problems like soil variation, irrigation problems, and pest and fungal infections. The mixture of these factors permits the farmer to distinguish between healthy and unhealthy plants, a distinction not always clearly visible. Thus, these views will aid in evaluating crop growth and production. The project is to develop an autonomous flying quadcopter, equipped with a GPS tracking system and programmed to be able to fly autonomously from one location to a different location using GPS coordinates. Its goal is to act as a proof-ofconcept for a small scale autonomous geographical region mapping (agricultural field in this case) and determine plant health using the Visible Atmospherically Resistant Index algorithm.

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“…It is worth mentioning that photogrammetric equipment of this kind can collect images on a very large area; however, this involves a high fi nancial outlay (Ahmad, 2011). We are now witnessing a great demand for small area mapping, yet the application of the classical forms of image acquisition makes the process very costly and therefore unfeasible (Ahmad and Samad, 2010). Photogrammetric products obtained using unmanned aerial vehicles are a great alternative to satellite and aerial images (Zhang and Kovacs, 2012).…”

Section: Introductionmentioning

confidence: 99%

Low aerial imagery – an assessment of georeferencing errors and the potential for use in environmental inventory

Smaczyński¹,

Medyńska-Gulij²

2017

Geodesy and Cartography

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Unmanned aerial vehicles are increasingly being used in close range photogrammetry. Real-time observation of the Earth’s surface and the photogrammetric images obtained are used as material for surveying and environmental inventory. The following study was conducted on a small area (approximately 1 ha). In such cases, the classical method of topographic mapping is not accurate enough. The geodetic method of topographic surveying, on the other hand, is an overly precise measurement technique for the purpose of inventorying the natural environment components. The author of the following study has proposed using the unmanned aerial vehicle technology and tying in the obtained images to the control point network established with the aid of GNSS technology. Georeferencing the acquired images and using them to create a photogrammetric model of the studied area enabled the researcher to perform calculations, which yielded a total root mean square error below 9 cm. The performed comparison of the real lengths of the vectors connecting the control points and their lengths calculated on the basis of the photogrammetric model made it possible to fully confirm the RMSE calculated and prove the usefulness of the UAV technology in observing terrain components for the purpose of environmental inventory. Such environmental components include, among others, elements of road infrastructure, green areas, but also changes in the location of moving pedestrians and vehicles, as well as other changes in the natural environment that are not registered on classical base maps or topographic maps.

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Aerial mapping using high resolution digital camera and unmanned aerial vehicle for Geographical Information System

Cited by 27 publications

References 0 publications

Localization and navigation of a mobile robot in an office-like environment

Localization and navigation of a mobile robot in an office-like environment

Design and Development of Agricultural Drone for Plant Health Analysis

Low aerial imagery – an assessment of georeferencing errors and the potential for use in environmental inventory

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