The Feasibility of 3d Point Cloud Generation From Smartphones

Alsubaie, Naif Muidh; El‐Sheimy, Naser

doi:10.5194/isprs-archives-xli-b5-621-2016

Cited by 2 publications

(1 citation statement)

References 13 publications

(4 reference statements)

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“…For example, [12] develops a close-range Augmented Reality (AR) system for GIS Registration and [13] demonstrates direct georeferencing on Unmanned Aerial Vehicles (UAV). Moreover, [14], [15] and [16] use a smartphone as a Mobile Mapping System to georeference close range objects employing photogrammetric techniques, to overcome the low accuracy of navigation sensors [17], [18]. Furthermore, [19] uses smartphones effectively to georeference video streams and demonstrates how they can become geospatial video providers.…”

Section: Introductionmentioning

confidence: 99%

Crowd-Sourced Wildfire Spread Prediction With Remote Georeferencing Using Smartphones

Bogdos

Μανωλάκος

2019

IEEE Access

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Wildfires are natural hazards with severe consequences worryingly worsening for many climate-change affected regions of our planet. Unfortunately, technologies that can provide real-time fireline information, such as satellites, in-field sensors, and social media texts, exhibit low spatial/temporal resolution or cannot be deployed cost-effectively in widespread geographical areas. We present the design, development, and implementation of a novel software service, called CITISENS, which by exploiting commodity smartphone sensors allows ordinary citizens to easily georeference a fire-line in real-time and report its coordinates as they are photographing a wildfire. The location/orientation sensors and the camera are used to compute the view-ray of the smartphone, and a digital elevation model is employed to estimate the ray's intersection with the topography. We have tested the georeferencing accuracy obtained and it is to be on par with, or even better, than that of existing satellite wildfire hotspot services. When combined with FLogA, a flexible wildfire spread simulator we have also developed, CITISENS offers the following unique advantages: real-time prediction of burn probabilities, dynamic assimilation of citizen-reported hotspots into ongoing simulations for improved predictive accuracy, and decision support to issue citizen alarms based on the estimated time-dependent risk at their location due to an approaching wildfire.

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Section: Introductionmentioning

confidence: 99%

Crowd-Sourced Wildfire Spread Prediction With Remote Georeferencing Using Smartphones

Bogdos

Μανωλάκος

2019

IEEE Access

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Improving the Accuracy of Direct Geo-referencing of Smartphone-Based Mobile Mapping Systems Using Relative Orientation and Scene Geometric Constraints

Alsubaie

Youssef

El‐Sheimy

2017

Sensors

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This paper introduces a new method which facilitate the use of smartphones as a handheld low-cost mobile mapping system (MMS). Smartphones are becoming more sophisticated and smarter and are quickly closing the gap between computers and portable tablet devices. The current generation of smartphones are equipped with low-cost GPS receivers, high-resolution digital cameras, and micro-electro mechanical systems (MEMS)-based navigation sensors (e.g., accelerometers, gyroscopes, magnetic compasses, and barometers). These sensors are in fact the essential components for a MMS. However, smartphone navigation sensors suffer from the poor accuracy of global navigation satellite System (GNSS), accumulated drift, and high signal noise. These issues affect the accuracy of the initial Exterior Orientation Parameters (EOPs) that are inputted into the bundle adjustment algorithm, which then produces inaccurate 3D mapping solutions. This paper proposes new methodologies for increasing the accuracy of direct geo-referencing of smartphones using relative orientation and smartphone motion sensor measurements as well as integrating geometric scene constraints into free network bundle adjustment. The new methodologies incorporate fusing the relative orientations of the captured images and their corresponding motion sensor measurements to improve the initial EOPs. Then, the geometric features (e.g., horizontal and vertical linear lines) visible in each image are extracted and used as constraints in the bundle adjustment procedure which correct the relative position and orientation of the 3D mapping solution.

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The Feasibility of 3d Point Cloud Generation From Smartphones

Cited by 2 publications

References 13 publications

Crowd-Sourced Wildfire Spread Prediction With Remote Georeferencing Using Smartphones

Crowd-Sourced Wildfire Spread Prediction With Remote Georeferencing Using Smartphones

Improving the Accuracy of Direct Geo-referencing of Smartphone-Based Mobile Mapping Systems Using Relative Orientation and Scene Geometric Constraints

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