Abstract
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.
Highlights
Structure from motion-multiview stereo (SfM-MVS) photogrammetry workflows allow the construction of high-resolution 3D models of landforms by matching narrow baseline partly overlapping aerial and/or terrestrial photo surveys [1,2,3,4]
For models consist of 2.15 × points (Model 1), the sum of rotation angles passes from almost 12◦ for the global navigation satellite system (GNSS) model, to less than 6◦ for many finely registered camera position (FRCP) and registered camera position (RCP) sub-models
For Model 2, the sum of the rotation components passes from 6◦ for the GNSS model to less than 2◦ for the FRCP sub-models, down to less than 1◦ for some of these sub-models
Summary
Structure from motion-multiview stereo (SfM-MVS) photogrammetry workflows allow the construction of high-resolution 3D models of landforms by matching narrow baseline partly overlapping aerial and/or terrestrial photo surveys [1,2,3,4]. Model building initiates with the acquisition of image data and encompasses image key point detection and matching, camera pose and sparse scene estimation (i.e., structure from motion), multiview reconstruction of a dense point cloud, as well as mesh tessellation and texturing, producing a model with arbitrary scaling, translation, and orientation. This component of the workflow is typically achieved through the use of proprietary software tools, with the main user impact on reconstruction quality being the characteristics of the input image data. Three solutions exist: (1) the use of the extrinsic camera parameters (i.e., the position and the orientation of photographs [15,16], (2) the use of ground control points (GCPs) (i.e., key points included in the reconstructed scene, for which the positions have been accurately measured, often using survey-grade Global Navigation Satellite System (GNSS) equipment [9,17,18,19,20], and (3) model rotation using reference surfaces on the model [21]
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