An Improved Method of Refining Relative Orientation in Global Structure from Motion with a Focus on Repetitive Structure and Very Short Baselines

2020 ◽  
Vol 86 (5) ◽  
pp. 299-315
Author(s):  
X. Wang ◽  
C. Heipke
Keyword(s):  
Structure From Motion ◽  
Relative Orientation ◽  
Global Structure ◽  
Conjugate Points ◽  
Data Sets ◽  
Computational Speed ◽  
Repetitive Structure ◽  
Close Range ◽  
Short Baselines ◽  
Image Pairs

Recently, global structure from motion has successfully gained many followers, mainly because of its computational speed. Most of these global methods take the parameters of relative orientation (ROs ) as input and then perform averaging operations. Therefore, eliminating incorrect ROs is of great significance for improving the robustness of global structure from motion. In this article, we propose a method to eliminate wrong ROs which have resulted from repetitive structure and very short baselines. We present two corresponding criteria that indicate the quality of ROs. Repetitive structure is detected based on counts of conjugate points of the various image pairs, while very short baselines are found by inspecting the intersection angles of corresponding image rays. By analyzing these two criteria, we detect and eliminate incorrect ROs. As correct ROs of image pairs with a longer baseline nearly parallel to both viewing directions can be valuable, a method to identify and keep these ROs is also part of our approach. We demonstrate the new method on various data sets, including public benchmarks as well as close-range images and images from unmanned aerial vehicles, by inserting our refined ROs into a global structure-from-motion pipeline. The experiments show that compared to other methods, we can generate the best results.

scholarly journals AUTOMATIC GEOREFERNCING OF CLOSE-RANGE FAÇADE IMAGES ACQUIRED IN AN NARROW AND LONG ALLEYWAY USING RTK DRONE IMAGES

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 63-68
Author(s):  
K. Park ◽  
S. Ham ◽  
I. Lee
Keyword(s):  
Structure From Motion ◽  
3D Model ◽  
Urban Regeneration ◽  
3D Models ◽  
Conjugate Points ◽  
Absolute Distance ◽  
Reference Images ◽  
Close Range ◽  
The Difference ◽  
The City

Abstract. The city of Seoul has selected Sewoon market building and its surrounding district as part of the urban regeneration zone, and currently has been promoting the project. To monitor results of the project regularly, the city has been trying to utilize a 3 dimension model of the area. In the case of buildings placed in narrow alleyways in the district, however, it is limited to generate 3D model of the buildings due to some factors. Therefore, in this study, a 3D model of façade of the building was created, using a RTK drone and action camera only. First method is to estimate of location of conjugate points using Structure from Motion, after setting conjugate points between images of the drone. Second method is to georeference action camera images by setting drone images as the reference images itself without the process of estimating location of the conjugate points. As a result of preliminary experiments to verify the two methods, the error of each method did not exceed a maximum of 0.030 m. Based on the result, we created 3D models of façade of the building in the alleyway, which is located at the intersection of Donhwamoon-ro 2 gil and Jong-ro 24 gil, and calculated absolute distance between the models. And the comparison showed that the difference was about 0.010 m on average.

scholarly journals Structure from Motion Point Clouds for Structural Monitoring

2019 ◽  
Vol 11 (16) ◽  
pp. 1940 ◽  
Author(s):  
Fausto Mistretta ◽  
Giannina Sanna ◽  
Flavio Stochino ◽  
Giuseppina Vacca
Keyword(s):  
Structure From Motion ◽  
Point Clouds ◽  
Structural Deformation ◽  
Beam Loading ◽  
Close Range Photogrammetry ◽  
Laser Scanners ◽  
Dense Point ◽  
Close Range ◽  
Bending Loading ◽  
Geometric Primitives

Dense point clouds acquired from Terrestrial Laser Scanners (TLS) have proved to be effective for structural deformation assessment. In the last decade, many researchers have defined methodology and workflow in order to compare different point clouds, with respect to each other or to a known model, assessing the potentialities and limits of this technique. Currently, dense point clouds can be obtained by Close-Range Photogrammetry (CRP) based on a Structure from Motion (SfM) algorithm. This work reports on a comparison between the TLS technique and the Close-Range Photogrammetry using the Structure from Motion algorithm. The analysis of two Reinforced Concrete (RC) beams tested under four-points bending loading is presented. In order to measure displacement distributions, point clouds at different beam loading states were acquired and compared. A description of the instrumentation used and the experimental environment, along with a comprehensive report on the calculations and results obtained is reported. Two kinds of point clouds comparison were investigated: Mesh to mesh and modeling with geometric primitives. The comparison between the mesh to mesh (m2m) approach and the modeling (m) one showed that the latter leads to significantly better results for both TLS and CRP. The results obtained with the TLS for both m2m and m methodologies present a Root Mean Square (RMS) levels below 1 mm, while the CRP method yields to an RMS level of a few millimeters for m2m, and of 1 mm for m.

scholarly journals An Adaptive Ellipse Distance Density Peak Fuzzy Clustering Algorithm Based on the Multi-target Traffic Radar

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4920
Author(s):  
Lin Cao ◽  
Xinyi Zhang ◽  
Tao Wang ◽  
Kangning Du ◽  
Chong Fu
Keyword(s):  
Euclidean Distance ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Search Algorithm ◽  
Measurement Data ◽  
Data Sets ◽  
Density Peak ◽  
Close Range ◽  
Decision Graph ◽  
Membership Matrix

In the multi-target traffic radar scene, the clustering accuracy between vehicles with close driving distance is relatively low. In response to this problem, this paper proposes a new clustering algorithm, namely an adaptive ellipse distance density peak fuzzy (AEDDPF) clustering algorithm. Firstly, the Euclidean distance is replaced by adaptive ellipse distance, which can more accurately describe the structure of data obtained by radar measurement vehicles. Secondly, the adaptive exponential function curve is introduced in the decision graph of the fast density peak search algorithm to accurately select the density peak point, and the initialization of the AEDDPF algorithm is completed. Finally, the membership matrix and the clustering center are calculated through successive iterations to obtain the clustering result.The time complexity of the AEDDPF algorithm is analyzed. Compared with the density-based spatial clustering of applications with noise (DBSCAN), k-means, fuzzy c-means (FCM), Gustafson-Kessel (GK), and adaptive Euclidean distance density peak fuzzy (Euclid-ADDPF) algorithms, the AEDDPF algorithm has higher clustering accuracy for real measurement data sets in certain scenarios. The experimental results also prove that the proposed algorithm has a better clustering effect in some close-range vehicle scene applications. The generalization ability of the proposed AEDDPF algorithm applied to other types of data is also analyzed.

scholarly journals Short communication: Challenges and applications of structure-from-motion photogrammetry in a physical model of a braided river

2019 ◽  
Vol 7 (1) ◽  
pp. 97-106 ◽  
Author(s):  
Pauline Leduc ◽  
Sarah Peirce ◽  
Peter Ashmore
Keyword(s):  
Structure From Motion ◽  
Short Communication ◽  
Successful Implementation ◽  
Braided River ◽  
Large Dataset ◽  
Automatic Target Detection ◽  
Braided Rivers ◽  
Digital Elevation ◽  
Communication Challenges ◽  
Close Range

Abstract. For extending the applications of structure-from-motion (SfM) photogrammetry in river flumes, we present the main challenges and methods used to collect a large dataset (>1000 digital elevation models, DEMs) of high-quality topographic data using close-range SfM photogrammetry with a resulting vertical precision of ∼1 mm. Automatic target detection, batch processing, and considerations for image quality were fundamental to the successful implementation of the SfM technique on such a large dataset, which was used primarily for capturing details of gravel-bed braided river morphodynamics and sedimentology. While the applications of close-range SfM photogrammetry are numerous, we include sample results from DEM differencing, which was used to quantify morphology change and provide estimates of water depth in braided rivers, as well as image analysis for mapping bed surface texture. These methods and results contribute to the growing field of SfM applications in geomorphology and close-range experimental settings in general.

scholarly journals Automated Target-Free Network Orienation and Camera Calibration

2014 ◽  
Vol II-5 ◽  
pp. 339-346 ◽  
Author(s):  
C. Stamatopoulos ◽  
C. S. Fraser
Keyword(s):  
Camera Calibration ◽  
Open Source Software ◽  
Focal Length ◽  
Relative Orientation ◽  
Aerial Imagery ◽  
Free Network ◽  
Close Range ◽  
Feature Based ◽  
Image Orientation ◽  
Matching Techniques

Automated close-range photogrammetric network orientation and camera calibration has traditionally been associated with the use of coded targets in the object space to allow for an initial relative orientation (RO) and subsequent spatial resection of the images. However, over the last decade, advances coming mainly from the computer vision (CV) community have allowed for fully automated orientation via feature-based matching techniques. There are a number of advantages in such methodologies for various types of applications, as well as for cases where the use of artificial targets might be not possible or preferable, for example when attempting calibration from low-level aerial imagery, as with UAVs, or when calibrating long-focal length lenses where small image scales call for inconveniently large coded targets. While there are now a number of CV-based algorithms for multi-image orientation within narrow-baseline networks, with accompanying open-source software, from a photogrammetric standpoint the results are typically disappointing as the metric integrity of the resulting models is generally poor, or even unknown. The objective addressed in this paper is target-free automatic multi-image orientation, maintaining metric integrity, within networks that incorporate wide-baseline imagery. The focus is on both the development of a methodology that overcomes the shortcomings that can be present in current CV algorithms, and on the photogrammetric priorities and requirements that exist in current processing pipelines. This paper also reports on the application of the proposed methodology to automated target-free camera self-calibration and discusses the process via practical examples.

scholarly journals Comparison of UAS-Based Structure-from-Motion and LiDAR for Structural Characterization of Short Broadacre Crops

2021 ◽  
Vol 13 (19) ◽  
pp. 3975
Author(s):  
Fei Zhang ◽  
Amirhossein Hassanzadeh ◽  
Julie Kikkert ◽  
Sarah Jane Pethybridge ◽  
Jan van Aardt
Keyword(s):  
Structure From Motion ◽  
Field Measurements ◽  
Point Clouds ◽  
Multiscale Model ◽  
Data Sets ◽  
Crop Height ◽  
Ground Control Points ◽  
Digital Elevation ◽  
Row Width ◽  
Model Cloud

The use of small unmanned aerial system (UAS)-based structure-from-motion (SfM; photogrammetry) and LiDAR point clouds has been widely discussed in the remote sensing community. Here, we compared multiple aspects of the SfM and the LiDAR point clouds, collected concurrently in five UAS flights experimental fields of a short crop (snap bean), in order to explore how well the SfM approach performs compared with LiDAR for crop phenotyping. The main methods include calculating the cloud-to-mesh distance (C2M) maps between the preprocessed point clouds, as well as computing a multiscale model-to-model cloud comparison (M3C2) distance maps between the derived digital elevation models (DEMs) and crop height models (CHMs). We also evaluated the crop height and the row width from the CHMs and compared them with field measurements for one of the data sets. Both SfM and LiDAR point clouds achieved an average RMSE of ~0.02 m for crop height and an average RMSE of ~0.05 m for row width. The qualitative and quantitative analyses provided proof that the SfM approach is comparable to LiDAR under the same UAS flight settings. However, its altimetric accuracy largely relied on the number and distribution of the ground control points.

scholarly journals Short Communication: Challenges and Applications of Structure-from-Motion Photogrammetry in a Physical Model of a Braided River

2018 ◽  
Author(s):  
Pauline Leduc ◽  
Sarah Peirce ◽  
Peter Ashmore
Keyword(s):  
Structure From Motion ◽  
Short Communication ◽  
Successful Implementation ◽  
Braided River ◽  
Large Dataset ◽  
Automatic Target Detection ◽  
Braided Rivers ◽  
Digital Elevation ◽  
Communication Challenges ◽  
Close Range

Abstract. Extending the applications of Structure-from-Motion (SfM) photogrammetry in river flumes, we present the main challenges and methods used to collect a large dataset (> 1000 digital elevation models) of high-quality topographic data using close-range SfM photogrammetry with a resulting vertical precision of ~ 1 mm. Automatic target-detection, batch processing, and considerations for image quality were fundamental to successful implementation of SfM on such a large dataset, which was used primarily for capturing details of gravel-bed braided river morphodynamics and sedimentology. While the applications of close-range SfM photogrammetry are numerous, we include sample results from DEM differencing, which was used to quantify morphology change and provide estimates of water depth in braided rivers, as well as image analysis for mapping bed surface texture. These methods and results contribute to the growing field of SfM applications in geomorphology and close-range experimental settings in general.

scholarly journals Performance Evaluation of Alternative Relative Orientation Procedures for UAV-based Imagery with Prior Flight Trajectory Information

2016 ◽  
Vol XLI-B3 ◽  
pp. 21-25 ◽  
Author(s):  
F. He ◽  
A. Habib
Keyword(s):  
Low Cost ◽  
Closed Form Solution ◽  
Experimental Tests ◽  
Form Solution ◽  
Relative Orientation ◽  
Conjugate Points ◽  
Flying Height ◽  
Reliable Estimate ◽  
Comparative Performance ◽  
The Impact

Thanks to recent advances at the hardware (e.g., emergence of reliable platforms at low cost) and software (e.g., automated identification of conjugate points in overlapping images) levels, UAV-based 3D reconstruction has been widely used in various applications. However, mitigating the impact of outliers in automatically matched points in UAV imagery, especially when dealing with scenes that has poor and/or repetitive texture, remains to be a challenging task. In spite of the fact that existing literature has already demonstrated that incorporating prior motion information can play an important role in increasing the reliability of the matching process, there is a lack of methodologies that are mainly suited for UAV imagery. Assuming the availability of prior information regarding the trajectory of a UAV-platform, this paper presents a two-point approach for reliable estimation of Relative Orientation Parameters (ROPs) of UAV-based images. This approach is based on the assumption that the UAV platform is moving at a constant flying height while maintaining the camera in a nadir-looking orientation. For this flight scenario, a closed-form solution that can be derived using a minimum of two pairs of conjugate points is established. In order to evaluate the performance of the proposed approach, experimental tests using real stereo-pairs acquired from different UAV platforms have been conducted. The derived results from the comparative performance analysis against the Nistér five-point approach demonstrate that the proposed two-point approach is capable of providing reliable estimate of the ROPs from UAV-based imagery in the presence of poor and/or repetitive texture with high percentage of matching outliers.

scholarly journals Big data; sensor networks and remotely-sensed data for mapping; feature extraction from lidar

2018 ◽  
Vol 1 ◽  
pp. 1-4
Author(s):  
Lorato Tlhabano
Keyword(s):  
Feature Extraction ◽  
Big Data ◽  
Low Cost ◽  
High Tech ◽  
Test Case ◽  
Data Sets ◽  
Remotely Sensed Data ◽  
Dramatic Rise ◽  
Close Range ◽  
Laws And Policies

Unmanned aerial vehicles (UAVs) can be used for mapping in the close range domain, combining aerial and terrestrial photogrammetry and now the emergence of affordable platforms to carry these technologies has opened up new opportunities for mapping and modeling cadastral boundaries. At the current state mainly low cost UAVs fitted with sensors are used in mapping projects with low budgets, the amount of data produced by the UAVs can be enormous hence the need for big data techniques’ and concepts. The past couple of years have witnessed the dramatic rise of low-cost UAVs fitted with high tech Lidar sensors and as such the UAVS have now reached a level of practical reliability and professionalism which allow the use of these systems as mapping platforms. UAV based mapping provides not only the required accuracy with respect to cadastral laws and policies as well as requirements for feature extraction from the data sets and maps produced, UAVs are also competitive to other measurement technologies in terms of economic aspects. In the following an overview on how the various technologies of UAVs, big data concepts and lidar sensor technologies can work together to revolutionize cadastral mapping particularly in Africa and as a test case Botswana in particular will be used to investigate these technologies. These technologies can be combined to efficiently provide cadastral mapping in difficult to reach areas and over large areas of land similar to the Land Administration Procedures, Capacity and Systems (LAPCAS) exercise which was recently undertaken by the Botswana government, we will show how the uses of UAVS fitted with lidar sensor and utilizing big data concepts could have reduced not only costs and time for our government but also how UAVS could have provided more detailed cadastral maps.

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