Wide Baseline Research Articles

Wide baseline stereovision based obstacle detection for unmanned surface vehicles

Wide baseline stereovision based obstacle detection for unmanned surface vehicles

Long-Range 3D Reconstruction Based on Flexible Configuration Stereo Vision Using Multiple Aerial Robots

Aerial robots, or unmanned aerial vehicles (UAVs), are widely used in 3D reconstruction tasks employing a wide range of sensors. In this work, we explore the use of wide baseline and non-parallel stereo vision for fast and movement-efficient long-range 3D reconstruction with multiple aerial robots. Each viewpoint of the stereo vision system is distributed on separate aerial robots, facilitating the adjustment of various parameters, including baseline length, configuration axis, and inward yaw tilt angle. Additionally, multiple aerial robots with different sets of parameters can be used simultaneously, including the use of multiple baselines, which allows for 3D monitoring at various depth ranges simultaneously, and the combined use of horizontal and vertical stereo, which improves the quality and completeness of depth estimation. Depth estimation at a distance of up to 400 m with less than 10% error using only 10 m of active flight distance is demonstrated in the simulation. Additionally, estimation of a distance of up to 100 m with flight distance of up to 10 m on the vertical axis and horizontal axis is demonstrated in an outdoor mapping experiment using the developed prototype UAVs.

Wide-Baseline Light Fields using Ellipsoidal Mirrors.

Traditional hand-held light field cameras only observe a small fraction of the cone of light emitted by a scene point. As a consequence, the study of interesting angular effects like iridescence are beyond the scope of such cameras. This paper envisions a new design for sensing light fields with wide baselines, so as to sense a significantly larger fraction of the cone of light emitted by scene points. Our system achieves this by imaging the scene, indirectly, through an ellipsoidal mirror. We show that an ellipsoidal mirror maps a wide cone of light from locations near one of its foci to a narrower cone at its other focus; thus, by placing a conventional light field camera at a focus, we can observe a wide-baseline light field from the scene near the other focus. We show via simulations and a lab prototype that wide-baseline light fields excel in the traditional applications involving changes in focus and perspective. Additionally, the larger cone of light that they observe allows the study of iridescence and thin-film interference. Perhaps surprisingly, the larger cone of light allows us to estimate surface normals of scene points by reasoning about their visibility.

Fast Data Generation for Training Deep-Learning 3D Reconstruction Approaches for Camera Arrays.

In the last decade, many neural network algorithms have been proposed to solve depth reconstruction. Our focus is on reconstruction from images captured by multi-camera arrays which are a grid of vertically and horizontally aligned cameras that are uniformly spaced. Training these networks using supervised learning requires data with ground truth. Existing datasets are simulating specific configurations. For example, they represent a fixed-size camera array or a fixed space between cameras. When the distance between cameras is small, the array is said to be with a short baseline. Light-field cameras, with a baseline of less than a centimeter, are for instance in this category. On the contrary, an array with large space between cameras is said to be of a wide baseline. In this paper, we present a purely virtual data generator to create large training datasets: this generator can adapt to any camera array configuration. Parameters are for instance the size (number of cameras) and the distance between two cameras. The generator creates virtual scenes by randomly selecting objects and textures and following user-defined parameters like the disparity range or image parameters (resolution, color space). Generated data are used only for the learning phase. They are unrealistic but can present concrete challenges for disparity reconstruction such as thin elements and the random assignment of textures to objects to avoid color bias. Our experiments focus on wide-baseline configuration which requires more datasets. We validate the generator by testing the generated datasets with known deep-learning approaches as well as depth reconstruction algorithms in order to validate them. The validation experiments have proven successful.

Content-Aware Warping for View Synthesis.

Existing image-based rendering methods usually adopt depth-based image warping operation to synthesize novel views. In this paper, we reason the essential limitations of the traditional warping operation to be the limited neighborhood and only distance-based interpolation weights. To this end, we propose content-aware warping, which adaptively learns the interpolation weights for pixels of a relatively large neighborhood from their contextual information via a lightweight neural network. Based on this learnable warping module, we propose a new end-to-end learning-based framework for novel view synthesis from a set of input source views, in which two additional modules, namely confidence-based blending and feature-assistant spatial refinement, are naturally proposed to handle the occlusion issue and capture the spatial correlation among pixels of the synthesized view, respectively. Besides, we also propose a weight-smoothness loss term to regularize the network. Experimental results on light field datasets with wide baselines and multi-view datasets show that the proposed method significantly outperforms state-of-the-art methods both quantitatively and visually. The source code is publicly available at https://github.com/MantangGuo/CW4VS.

Characterization of the effects of right ventricular and left bundle branch area pacing on the QRS complex

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): TED2021-130459B-I00 LMP94_21 Introduction Right ventricular pacing (RVP) has been the treatment of choice for patients with atrioventricular block and sinus node disease for the last decades . It is well known that RVP ventricular may cause ventricular dyssynchrony and could lead to left ventricular dysfunction in 20% of patients, and therefore could increase the risk of heart failure, atrial fibrillation and cardiovascular mortality. Left bundle branch area pacing (LBBAP) has recently emerged as a new physiological pacing modality that can circumvent the limitations of RVP. The purpose of this study was to compare changes induced by RVP and LBBAP in the standard 12-lead ECG by the analysis of QRS complex characteristics in order to asses ventricular dyssynchrony. Methods ECG recordings from 134 patients (82 LBBAP, 52 RVP) with indication for anti-bradycardia therapy were acquired before and after pacemaker implantation. Patients were grouped into narrow and wide baseline QRS using a threshold for the QRS duration of 120 ms. Median beats were calculated at baseline and 24 hours post-implantation states for each patient. ECG markers describing ventricular activation were measured, including: 1) QRS duration (QRSd) from QRS onset to QRS end; 2) QRS area (QRSa), computed from the individual areas in orthogonal X, Y and Z leads calculated using the Kors conversion matrix; 3) a new index QRS50 proposed to estimate the ventricular activation time from the first three components of the singular value decomposition of the median QRS complex. Results Note that the QRS characteristics showed similar values in baseline for both pacing techniques. In patients with narrow QRS, we found that paced QRS characteristics remained similar from the basal QRS in LBBAP group whereas in RVP group they worsened. The three markers, QRSd, QRSa and QRS50, increased after RVP by 34 ms, 68 uVs and 22 ms, respectively (p<0,001). In patients with wide QRS, LBBAP significantly reduced QRSd, QRSa and QRS50 while RVP increased QRSa and QRS50 and left QRSd unchanged. Conclusion In patients receiving a pacemaker for anti-bradycardia therapy, LBBAP led to more synchronized ventricular activation than RVP measured from QRS duration- and area-related characteristics.

VoxelTrack: Multi-Person 3D Human Pose Estimation and Tracking in the Wild.

We present VoxelTrack for multi-person 3D pose estimation and tracking from a few cameras which are separated by wide baselines. It employs a multi-branch network to jointly estimate 3D poses and re-identification (Re-ID) features for all people in the environment. In contrast to previous efforts which require to establish cross-view correspondence based on noisy 2D pose estimates, it directly estimates and tracks 3D poses from a 3D voxel-based representation constructed from multi-view images. We first discretize the 3D space by regular voxels and compute a feature vector for each voxel by averaging the body joint heatmaps that are inversely projected from all views. We estimate 3D poses from the voxel representation by predicting whether each voxel contains a particular body joint. Similarly, a Re-ID feature is computed for each voxel which is used to track the estimated 3D poses over time. The main advantage of the approach is that it avoids making any hard decisions based on individual images. The approach can robustly estimate and track 3D poses even when people are severely occluded in some cameras. It outperforms the state-of-the-art methods by a large margin on four public datasets including Shelf, Campus, Human3.6 M and CMU Panoptic.

GraffMatch: Global Matching of 3D Lines and Planes for Wide Baseline LiDAR Registration

Using geometric landmarks like lines and planes can increase navigation accuracy and decrease map storage requirements compared to commonly-used LiDAR point cloud maps. However, landmark-based registration for applications like loop closure detection is challenging because a reliable initial guess is not available. Global landmark matching has been investigated in the literature, but these methods typically use ad hoc representations of 3D line and plane landmarks that are not invariant to large viewpoint changes, resulting in incorrect matches and high registration error. To address this issue, we adopt the affine Grassmannian manifold to represent 3D lines and planes and prove that the distance between two landmarks is invariant to rotation and translation if a shift operation is performed before applying the Grassmannian metric. This invariance property enables the use of our graph-based data association framework for identifying landmark matches that can subsequently be used for registration in the least-squares sense. Evaluated on a challenging landmark matching and registration task using publicly-available LiDAR datasets, our approach yields a 1.7x and 3.5x improvement in successful registrations compared to methods that use viewpoint-dependent centroid and "closest point" representations, respectively.

A High Dynamic Range Imaging Method for Short Exposure Multiview Images

The restoration and enhancement of multiview low dynamic range (MVLDR) images captured in low lighting conditions is a great challenge. The disparity maps are hardly reliable in practical, real-world scenarios and suffers from holes and artifacts due to large baseline and angle deviation among multiple cameras in low lighting conditions. Furthermore, multiple images with some additional information (e.g., ISO/exposure time, etc.) are required for the radiance map and poses the additional challenges of deghosting to encounter motion artifacts. In this paper, we proposed a method to reconstruct multiview high dynamic range (MVHDR) images from MVLDR images without relying on disparity maps. We detect and accurately match the feature points among the involved input views and gather the brightness information from the neighboring viewpoints to optimize an image restoration function based on input exposure gain to finally generate MVHDR images. Our method is very reliable and suitable for a wide baseline among sparse cameras. The proposed method requires only one image per viewpoint without any additional information and outperforms others.

The ultraviolet continuum slopes (β) of galaxies at z ≃ 8-16 from JWST and ground-based near-infrared imaging

ABSTRACT We study the rest-frame ultraviolet (UV) continuum slopes (β) of galaxies at redshifts 8 < z < 16 (〈z〉 = 10), using a combination of JWST ERO and ERS NIRCam imaging and ground-based near-infrared imaging of the COSMOS field. The combination of JWST and ground-based imaging provides a wide baseline in both redshift and absolute UV magnitude (−22.6 < MUV < −17.9), sufficient to allow a meaningful comparison to previous results at lower redshift. Using a power-law fitting technique, we find that our full sample (median MUV = −19.3 ± 1.3) returns an inverse-variance weighted mean value of 〈β〉 = −2.10 ± 0.05, with a corresponding median value of β = −2.29 ± 0.09. These values imply that the UV colours of galaxies at z > 8 are, on average, no bluer than the bluest galaxies in the local universe (e.g. NGC 1705; β = −2.46). We find evidence for a β − MUV relation, such that brighter UV galaxies display redder UV slopes ($\rm {d}\beta / \rm {d} M_{\rm UV} = -0.17 \pm 0.05$). Comparing to results at lower redshift, we find that the slope of our β − MUV relation is consistent with the slope observed at z ≃ 5 and that, at a given MUV, our 8 < z < 16 galaxies are bluer than their z ≃ 5 counterparts, with an inverse-variance weighted mean offset of 〈Δβ〉 = −0.38 ± 0.09. We do not find strong evidence that any objects in our sample display ultra-blue UV continuum slopes (i.e. β ≲ −3) that would require their UV emission to be dominated by ultra-young, dust-free stellar populations with high Lyman-continuum escape fractions. Comparing our results to the predictions of theoretical galaxy formation models, we find that the galaxies in our sample are consistent with the young, metal-poor, and moderately dust-reddened galaxies expected at z > 8.

Orbital and dynamical analysis of the system around HR 8799

Context. HR 8799 is a young planetary system composed of four planets and a double debris belt. Being the first multi-planetary system discovered with the direct imaging technique, it has been observed extensively since 1998. This wide baseline of astrometric measurements, counting over 50 observations in 20 years, permits a detailed orbital and dynamical analysis of the system. Aims. To explore the orbital parameters of the planets, their dynamical history, and the planet-to-disk interaction, we made follow-up observations of the system during the VLT/SPHERE guaranteed time observation program. We obtained 21 observations, most of them in favorable conditions. In addition, we observed HR 8799 with the instrument LUCI at the Large Binocular Telescope (LBT). Methods. All the observations were reduced with state-of-the-art algorithms implemented to apply the spectral and angular differential imaging method. We re-reduced the SPHERE data obtained during the commissioning of the instrument and in three open-time programs to have homogeneous astrometry. The precise position of the four planets with respect to the host star was calculated by exploiting the fake negative companions method. We obtained an astrometric precision of the order of 6 mas in the worst case and 1 mas in the best case. To improve the orbital fitting, we also took into account all of the astrometric data available in the literature. From the photometric measurements obtained in different wavelengths, we estimated the masses of the planets following the evolutionary models. Results. We obtained updated parameters for the orbits with the assumption of coplanarity, relatively small eccentricities, and periods very close to the 2:1 resonance. We also refined the dynamical mass of each planet and the parallax of the system (24.49 ± 0.07 mas), which overlap with the recent Gaia eDR3/DR3 estimate. Hydrodynamical simulations suggest that inward migration of the planets caused by the interaction with the disk might be responsible for the planets being locked in resonance. We also conducted detailed N-body simulations indicating possible positions of a putative fifth planet with a mass below the present detection limits of ≃3 MJup.

Danish Airs and Grounds: A Dataset for Aerial-to-Street-Level Place Recognition and Localization

Place recognition and visual localization are particularly challenging in wide baseline configurations. In this letter, we contribute with the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Danish Airs and Grounds</i> (DAG) dataset, a large collection of street-level and aerial images targeting such cases. Its main challenge lies in the extreme viewing-angle difference between query and reference images with consequent changes in illumination and perspective. The dataset is larger and more diverse than current publicly available data, including more than 50 km of roads in urban, suburban and rural areas. All images are associated with accurate 6-DoF metadata that allows the benchmarking of visual localization methods. Additionally, we validate our data by presenting the results of a simple map-to-image re-localization baseline. that first estimates a dense 3D reconstruction from the aerial images and then matches query street-level images to street-level renderings of the 3D model. The dataset can be downloaded at:.

3D Reconstruction Method of Space Target on Optical Images with Wide Baseline via Neural Radiance Field

3D reconstruction of space targets using optical measurement data is an important topic in the field of space surveillance and spacecraft service in orbit. The core of current mainstream 3D reconstruction methods is to establish feature association between optical images. In space-based optical imaging scenes, the texture of target region is missing and the range of view is large, which causes great difficulties in feature extraction and matching of image sequences. Neural radiance field technology does not need to extract explicit features, and its implicit expression of 3D scenes has great potential for solving reconstruction problems in weak texture and broad baseline cases. Therefore, a novel 3D reconstruction method for a sequence of images based on neural radiance field is proposed. The proposed method breaks through the limitation of traditional 3D reconstruction methods which rely on rich texture and narrow baseline image sequences. Simulation experiments show that compared with the existing methods for space target images with weak texture and wide baseline, our method performs better in terms of accuracy and completeness.

Late Neck Related Adverse Events are Rare Among Patients with Wide Aortic Neck Undergoing Endovascular Aneurysm Repair with the Ovation Endograft

Patients with wide aortic necks undergoing Endovascular Aneurysm Repair (EVAR) have been shown to be at a higher risk for neck-related complications. We aim to examine outcomes of EVAR with an endograft exerting minimal outward pressure (Ovation-Endologix) in patients with a large baseline neck diameter. We performed a retrospective single center study, including consecutive patients undergoing EVAR with the Ovation system from May 2011 to April 2021. Patients were divided in Groups 1 and 2 if the 20, 23, 26, 29 mm or the 34mm proximal diameter main body was used, respectively. According to the instructions for use of the device, for neck diameters 27-30mm the 34mm main body is required. Primary endpoint was rate of neckrelated complications during follow-up, (type Ia endoleak, migration >10mm and neck-related re-interventions) and rate of aortic neck dilatation (AND). AND was determined based on multiple aortic neck diameters that were recorded and compared between the 1-month computed tomography angiography (CTA) after EVAR and the last available follow-up CTA.Secondary endpoints were peri-procedural and follow-up outcomes such as endoleaks, reinterventions and overall mortality. In total 281 patients were included, 222 in Group 1 and 59 in Group 2. Patients in Group 2 presented significantly shorter neck length, higher neck angulation and more common reversed tapered configuration. Median follow-up was 36months (Range: 6-106). Early and late type Ia endoleak was observed in 4 and 2 patients in each group, respectively (P=0.063 and P=0.195, respectively). Distal migration was observed in 2 patients in Group 2 and AND was recorded in 2 patients in each group (P=0.195). Freedom from the primary endpoint was estimated at 98%, 94%, 94% at 12-, 36-, 60-months for Group 1 and at 98%, 95%, 86% for Group 2 (P-Value 0.266). Probability of survival was 95%, 86%, 75% at 12-, 36-, 60-months for Group 1 and 83%, 77%, 72% for Group 2 (P-Value 0.226).Multivariate regression analysis identified neither Group 1 versus Group 2 nor absolute value of aortic neck diameter as significant predictors of neck-related adverse events. Neck diameters did not display significant differences over time in any of the levels evaluated. EVAR with the Ovation endograft results in low rates of late neck related complications which is also true for patients with wide baseline aortic necks.

A Sparse Feature Matching Model Using a Transformer towards Large-View Indoor Visual Localization

Accurate indoor visual localization has been a challenging task under large-view scenes with wide baselines and weak texture images, where it is difficult to accomplish accurate image matching. To address the problem of sparse image features mismatching, we develop a coarse-to-fine feature matching model using a transformer, termed MSFA-T, which assigns the corresponding semantic labels to image features for an incipient coarse matching. To avoid the anomalous scoring of sparse feature interrelationship in the attention assigning phase, we propose a multiscale forward attention mechanism that decomposes the similarity-based features to learn the specificity of sparse features, the influence of position-independence on sparse features is reduced and the performance of the fine image matching in visual localization is effectively improved. We conduct extensive experiments on the challenging datasets; the results show that our model achieves image matching with an average 79.8% probability of the area under the cumulative curve of the corner point error, which outperforms the related state-of-the-art algorithms by an improvement of 13% probability at 1 m accuracy for the image-based visual localization in large view scenes.

Visualization pipeline of autonomous driving scenes based on FCCR-3D reconstruction

Visual sensor-based human–computer interaction can provide environmental perception data of traffic road scenes for mechatronic intelligent vehicles. The existing three-dimensional (3D) reconstruction visualization schemes are mainly based on structured scene reconstruction with multiview geometry and are offline operations. It is difficult for large-scale autonomous driving scenes with wide baselines to ensure consistent reconstruction results in the wide field-of-view area and the center of the light-of-sight area. It is also difficult to achieve uniform reconstruction results for regions with drastic depth changes and inconsistent texture fineness. Therefore, we propose a 3D reconstruction-based visualization pipeline for autonomous driving scenes of wide baseline. First, the stereo geometric model is initialized. The parameters of distortion correction and polar calibration are calibrated. Second, the proposed feature chain code resampling (FCCR) is used to uniformly extract the features for regions with inconsistent texture fineness and depth variation. The filtered 3D data are obtained based on the stereo geometric model. Finally, the texture mapping of the mesh skeleton of 3D data is carried out by the bilinear combination of surrounding multilevel gradual textures. The visualization results of scene rendering and scene roaming are realized. The pipeline has been effectively verified on the simulated experimental platform, real vehicle test, and public dataset. It can meet the real-time 3D reconstruction visualization of wide-baseline scenes. The reconstruction texture is uniform, and the detail response is rich. We can present the intuitive, 3D, real-time, graphical physical quantities of scene data for drivers, intelligent vehicles, AR-HUD, and control systems. The method we present has research value and application significance for the practical engineering of electronic images.

Prevalence and levels of Campylobacter in broiler chicken batches and carcasses in Ireland in 2017–2018

In 2008, an EU wide baseline survey of broilers revealed a high Campylobacter prevalence. To assist with industry-wide controls, updated data were required. The primary objective of this study was to establish up-to-date data on Campylobacter carriage and carcass contamination in Irish broilers. Monthly samples were collected from the three largest broiler processing plants in Ireland over a twelve-month period. Samples were taken from both first and final thin birds (partial and full depopulation) from 358 batches of broilers. From each batch, a composite sample of 10 caecal contents (n = 358) and 5 neck skins (n = 1790) were collected and numbers of Campylobacter in each sample were determined. Of the 1790 neck skin samples tested, 53% were Campylobacter positive. Campylobacter was detected in the caecal contents of 66% of all batches tested. Depopulation and/or age had a significant effect on Campylobacter prevalence with 67% of final thin broilers yielding Campylobacter-positive neck skin samples in contrast to 38% of first thin broilers that yielded positive neck skin samples (P ≤ 0.002). A significant seasonal variation was observed in the rate of Campylobacter-positive caecal samples with higher prevalence seen in July (85%) than the colder months of November (61%), December (50%), January (61%) March (57%) and April (59%). Neck skin samples were 7 times more likely to be Campylobacter positive if the caecal contents from the same batch were positive (odds ratio = 7.1; P ≤ 0.0001). The decrease in Campylobacter prevalence observed in neck skin and caecal contents demonstrates the improvements and progress made in reducing prevalences of this important enteropathogen in the Irish poultry industry since the 2008 EU baseline survey. It also provides further supporting data on the impact of thinning, the processing environment and season on Campylobacter prevalence.

Image stitching by disparity-guided multi-plane alignment

Image stitching aims to warp and align two or more images with overlapping areas to generate a panorama with a larger field of view. Due to the wide baseline or the abrupt depth changes caused by the foreground objects, the phenomenon that adjacent pixels or regions in one image are not adjacent in another image happens. It is difficult to avoid severe parallax artifacts and get good alignment results when stitching such images. In this paper, focusing on the images with large parallax, we design a multi-plane alignment algorithm guided by the disparity map. The concept of average tolerable parallax is proposed to help distinguish one background plane and multiple foreground objects from the image, which are robust to small parallax. In order to obtain the reliable disparity map of the image pairs got from any camera with unknown camera parameters, we propose an optimal homography estimation method based on the relative projection biases. This helps to satisfy the common epipolar line constraint when applying stereo matching modules. Experimental results demonstrate that our algorithm provides accurate stitching results on images with large parallax, and outperforms other existing state-of-the-art methods both qualitatively and quantitatively.

IMAGE ORIENTATION WITH A HYBRID PIPELINE ROBUST TO ROTATIONS AND WIDE-BASELINES

Abstract. The extraction of reliable and repeatable interest points among images is a fundamental step for automatic image orientation (Structure-From-Motion). Despite recent progresses, open issues in challenging conditions - such as wide baselines and strong light variations - are still present. Over the years, traditional hand-crafted methods have been paired by learning-based approaches, progressively updating the state-of-the-art according to recent benchmarks. Notwithstanding these advancements, learning-based methods are often not suitable for real photogrammetric surveys due to their lack of rotation invariance, a fundamental requirement for these specific applications. This paper proposes a novel hybrid image matching pipeline which employs both hand-crafted and deep-based components, to extract reliable rotational invariant keypoints optimized for wide-baseline scenarios. The proposed hybrid pipeline was compared with other hand-crafted and learning-based state-of-the-art approaches on some photogrammetric datasets using metric ground-truth data. Results show that the proposed hybrid matching pipeline has high accuracy and appeared to be the only method among the evaluated ones able to register images in the most challenging wide-baseline scenarios.

Relative Positioning in Remote Areas Using a GNSS Dual Frequency Smartphone.

The use of GPS positioning and navigation capabilities in mobile phones is present in our daily lives for more than a decade, but never with the centimeter level of precision that can actually be reached with several of the most recent smartphones. The introduction of the new GNSS systems (Global Navigation Satellite Systems), the European system Galileo, is opening new horizons in a wide range of areas that rely on precise georeferencing, namely the mass market smartphones apps. The constant growth of this market has brought new devices with innovative capabilities in hardware and software. The introduction of the Android 7 by Google, allowing access to the GNSS raw code and phase measurements, and the arrival of the new chip from Broadcom BCM47755 providing dual frequency in some smartphones came to revolutionize the positioning performance of these devices as never seen before. The Xiaomi Mi8 was the first smartphone to combine those features, and it is the device used in this work. It is well known that it is possible to obtain centimeter accuracy with this kind of device in relative static positioning mode with distances to a reference station up to a few tens of kilometers, which we also confirm in this paper. However, the main purpose of this work is to show that we can also get good positioning accuracy using long baselines. We used the ability of the Xiaomi Mi8 to get dual frequency code and phase raw measurements from the Galileo and GPS systems, to do relative static positioning in post-processing mode using wide baselines, of more than 100 km, to perform precise surveys. The results obtained were quite interesting with RMSE below 30 cm, showing that this type of smartphone can be easily used as a low-cost device, for georeferencing and mapping applications. This can be quite useful in remote areas where the CORS networks are not dense or even not available.