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Open AccessArticle

Photogrammetric 3D Model via Smartphone GNSS Sensor: Workflow, Error Estimate, and Best Practices

DISTAR, Università degli Studi di Napoli Federico II, Via Cupa Nuova Cintia, 21, 80126 Napoli, Italy
Consiglio Nazionale delle Ricerche, IGAG, c.o. Dipartimento di Scienze della Terra, Università di Roma Sapienza, P.le Aldo Moro 5, 00185 Roma, Italy
Geologist, Via Forno Vecchio, 38, Marigliano, 80034 Napoli, Italy
Department of Petroleum Engineering, Texas A&M University at Qatar, Education City, Doha PO Box 23874, Qatar
Dipartimento di Scienze della Terra, Sapienza Università di Roma, P.le Aldo Moro 5, 00185 Roma, Italy
Research Group ‘Geodesia’, Universidad Complutense de Madrid, 28040 Madrid, Spain
Department of Geological Sciences, University of Texas at El Paso, 500 W University Ave, El Paso, TX 79968, USA
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(21), 3616;
Received: 29 September 2020 / Revised: 1 November 2020 / Accepted: 2 November 2020 / Published: 4 November 2020
(This article belongs to the Special Issue GNSS for Geosciences)
Geotagged smartphone photos can be employed to build digital terrain models using structure from motion-multiview stereo (SfM-MVS) photogrammetry. Accelerometer, magnetometer, and gyroscope sensors integrated within consumer-grade smartphones can be used to record the orientation of images, which can be combined with location information provided by inbuilt global navigation satellite system (GNSS) sensors to geo-register the SfM-MVS model. The accuracy of these sensors is, however, highly variable. In this work, we use a 200 m-wide natural rocky cliff as a test case to evaluate the impact of consumer-grade smartphone GNSS sensor accuracy on the registration of SfM-MVS models. We built a high-resolution 3D model of the cliff, using an unmanned aerial vehicle (UAV) for image acquisition and ground control points (GCPs) located using a differential GNSS survey for georeferencing. This 3D model provides the benchmark against which terrestrial SfM-MVS photogrammetry models, built using smartphone images and registered using built-in accelerometer/gyroscope and GNSS sensors, are compared. Results show that satisfactory post-processing registrations of the smartphone models can be attained, requiring: (1) wide acquisition areas (scaling with GNSS error) and (2) the progressive removal of misaligned images, via an iterative process of model building and error estimation. View Full-Text
Keywords: SfM-MVS photogrammetry; 3D model registration; GNSS-Smartphone; GCPs alternative SfM-MVS photogrammetry; 3D model registration; GNSS-Smartphone; GCPs alternative
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    Description: This video illustrates step-by-step procedure for model construction and registration
MDPI and ACS Style

Tavani, S.; Pignalosa, A.; Corradetti, A.; Mercuri, M.; Smeraglia, L.; Riccardi, U.; Seers, T.; Pavlis, T.; Billi, A. Photogrammetric 3D Model via Smartphone GNSS Sensor: Workflow, Error Estimate, and Best Practices. Remote Sens. 2020, 12, 3616.

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