Mapping with Small UAS: A Point Cloud Accuracy Assessment

2015 ◽  
Vol 9 (4) ◽  
Author(s):  
Charles Toth ◽  
Grzegorz Jozkow ◽  
Dorota Grejner-Brzezinska
Keyword(s):  
Point Cloud ◽  
Accuracy Assessment ◽  
Point Clouds ◽  
Image Sensor ◽  
Flying Height ◽  
Attractive Alternative ◽  
Surface Model ◽  
Topographic Mapping ◽  
Main Question ◽  
Essential Information

AbstractInterest in using inexpensive Unmanned Aerial System (UAS) technology for topographic mapping has recently significantly increased. Small UAS platforms equipped with consumer grade cameras can easily acquire high-resolution aerial imagery allowing for dense point cloud generation, followed by surface model creation and orthophoto production. In contrast to conventional airborne mapping systems, UAS has limited ground coverage due to low flying height and limited flying time, yet it offers an attractive alternative to high performance airborne systems, as the cost of the sensors and platform, and the flight logistics, is relatively low. In addition, UAS is better suited for small area data acquisitions and to acquire data in difficult to access areas, such as urban canyons or densely built-up environments. The main question with respect to the use of UAS is whether the inexpensive consumer sensors installed in UAS platforms can provide the geospatial data quality comparable to that provided by conventional systems.This study aims at the performance evaluation of the current practice of UAS-based topographic mapping by reviewing the practical aspects of sensor configuration, georeferencing and point cloud generation, including comparisons between sensor types and processing tools. The main objective is to provide accuracy characterization and practical information for selecting and using UAS solutions in general mapping applications. The analysis is based on statistical evaluation as well as visual examination of experimental data acquired by a Bergen octocopter with three different image sensor configurations, including a GoPro HERO3+ Black Edition, a Nikon D800 DSLR and a Velodyne HDL-32. In addition, georeferencing data of varying quality were acquired and evaluated. The optical imagery was processed by using three commercial point cloud generation tools. Comparing point clouds created by active and passive sensors by using different quality sensors, and finally, by different commercial software tools, provides essential information for the performance validation of UAS technology.

scholarly journals COMPARISON OF UAS-BASED PHOTOGRAMMETRY SOFTWARE FOR 3D POINT CLOUD GENERATION: A SURVEY OVER A HISTORICAL SITE

2017 ◽  
Vol IV-4/W4 ◽  
pp. 55-61 ◽  
Author(s):  
F. Alidoost ◽  
H. Arefi
Keyword(s):  
Point Cloud ◽  
Accuracy Assessment ◽  
Point Clouds ◽  
Surface Model ◽  
Geospatial Information ◽  
Software Packages ◽  
Comparison Results ◽  
3D City Modelling ◽  
Time Acquisition

Nowadays, Unmanned Aerial System (UAS)-based photogrammetry offers an affordable, fast and effective approach to real-time acquisition of high resolution geospatial information and automatic 3D modelling of objects for numerous applications such as topography mapping, 3D city modelling, orthophoto generation, and cultural heritages preservation. In this paper, the capability of four different state-of-the-art software packages as 3DSurvey, Agisoft Photoscan, Pix4Dmapper Pro and SURE is examined to generate high density point cloud as well as a Digital Surface Model (DSM) over a historical site. The main steps of this study are including: image acquisition, point cloud generation, and accuracy assessment. The overlapping images are first captured using a quadcopter and next are processed by different software to generate point clouds and DSMs. In order to evaluate the accuracy and quality of point clouds and DSMs, both visual and geometric assessments are carry out and the comparison results are reported.

scholarly journals Assessment of DSM Based on Radiometric Transformation of UAV Data

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1649
Author(s):  
Muhammad Hamid Chaudhry ◽  
Anuar Ahmad ◽  
Qudsia Gulzar ◽  
Muhammad Shahid Farid ◽  
Himan Shahabi ◽  
...  
Keyword(s):  
Point Cloud ◽  
Accuracy Assessment ◽  
Low Cost ◽  
Image Data ◽  
Flying Height ◽  
Surface Model ◽  
Lidar Data ◽  
Local Accuracy ◽  
Uav Images ◽  
Uav Image

Unmanned Aerial Vehicle (UAV) is one of the latest technologies for high spatial resolution 3D modeling of the Earth. The objectives of this study are to assess low-cost UAV data using image radiometric transformation techniques and investigate its effects on global and local accuracy of the Digital Surface Model (DSM). This research uses UAV Light Detection and Ranging (LIDAR) data from 80 meters and UAV Drone data from 300 and 500 meters flying height. RAW UAV images acquired from 500 meters flying height are radiometrically transformed in Matrix Laboratory (MATLAB). UAV images from 300 meters flying height are processed for the generation of 3D point cloud and DSM in Pix4D Mapper. UAV LIDAR data are used for the acquisition of Ground Control Points (GCP) and accuracy assessment of UAV Image data products. Accuracy of enhanced DSM with DSM generated from 300 meters flight height were analyzed for point cloud number, density and distribution. Root Mean Square Error (RMSE) value of Z is enhanced from ±2.15 meters to 0.11 meters. For local accuracy assessment of DSM, four different types of land covers are statistically compared with UAV LIDAR resulting in compatibility of enhancement technique with UAV LIDAR accuracy.

A New Kinect-Based Scanning System and its Application

2015 ◽  
Vol 764-765 ◽  
pp. 1375-1379 ◽  
Author(s):  
Cheng Tiao Hsieh
Keyword(s):  
Point Cloud ◽  
Depth Map ◽  
Bilateral Filter ◽  
Point Clouds ◽  
Digital Fabrication ◽  
Surface Model ◽  
Scanning System ◽  
3D Registration ◽  
Noise Point

This paper aims at presenting a simple approach utilizing a Kinect-based scanner to create models available for 3D printing or other digital manufacturing machines. The outputs of Kinect-based scanners are a depth map and they usually need complicated computational processes to prepare them ready for a digital fabrication. The necessary processes include noise filtering, point cloud alignment and surface reconstruction. Each process may require several functions and algorithms to accomplish these specific tasks. For instance, the Iterative Closest Point (ICP) is frequently used in a 3D registration and the bilateral filter is often used in a noise point filtering process. This paper attempts to develop a simple Kinect-based scanner and its specific modeling approach without involving the above complicated processes.The developed scanner consists of an ASUS’s Xtion Pro and rotation table. A set of organized point cloud can be generated by the scanner. Those organized point clouds can be aligned precisely by a simple transformation matrix instead of the ICP. The surface quality of raw point clouds captured by Kinect are usually rough. For this drawback, this paper introduces a solution to obtain a smooth surface model. Inaddition, those processes have been efficiently developed by free open libraries, VTK, Point Cloud Library and OpenNI.

scholarly journals LiDAR-Aided Interior Orientation Parameters Refinement Strategy for Consumer-Grade Cameras Onboard UAV Remote Sensing Systems

2020 ◽  
Vol 12 (14) ◽  
pp. 2268
Author(s):  
Tian Zhou ◽  
Seyyed Meghdad Hasheminasab ◽  
Radhika Ravi ◽  
Ayman Habib
Keyword(s):  
Precision Agriculture ◽  
Point Cloud ◽  
Point Clouds ◽  
Bundle Adjustment ◽  
Flying Height ◽  
Control Points ◽  
Object Space ◽  
Area Of Interest ◽  
Refinement Strategy ◽  
Orientation Parameters

Unmanned aerial vehicles (UAVs) are quickly emerging as a popular platform for 3D reconstruction/modeling in various applications such as precision agriculture, coastal monitoring, and emergency management. For such applications, LiDAR and frame cameras are the two most commonly used sensors for 3D mapping of the object space. For example, point clouds for the area of interest can be directly derived from LiDAR sensors onboard UAVs equipped with integrated global navigation satellite systems and inertial navigation systems (GNSS/INS). Imagery-based mapping, on the other hand, is considered to be a cost-effective and practical option and is often conducted by generating point clouds and orthophotos using structure from motion (SfM) techniques. Mapping with photogrammetric approaches requires accurate camera interior orientation parameters (IOPs), especially when direct georeferencing is utilized. Most state-of-the-art approaches for determining/refining camera IOPs depend on ground control points (GCPs). However, establishing GCPs is expensive and labor-intensive, and more importantly, the distribution and number of GCPs are usually less than optimal to provide adequate control for determining and/or refining camera IOPs. Moreover, consumer-grade cameras with unstable IOPs have been widely used for mapping applications. Therefore, in such scenarios, where frequent camera calibration or IOP refinement is required, GCP-based approaches are impractical. To eliminate the need for GCPs, this study uses LiDAR data as a reference surface to perform in situ refinement of camera IOPs. The proposed refinement strategy is conducted in three main steps. An image-based sparse point cloud is first generated via a GNSS/INS-assisted SfM strategy. Then, LiDAR points corresponding to the resultant image-based sparse point cloud are identified through an iterative plane fitting approach and are referred to as LiDAR control points (LCPs). Finally, IOPs of the utilized camera are refined through a GNSS/INS-assisted bundle adjustment procedure using LCPs. Seven datasets over two study sites with a variety of geomorphic features are used to evaluate the performance of the developed strategy. The results illustrate the ability of the proposed approach to achieve an object space absolute accuracy of 3–5 cm (i.e., 5–10 times the ground sampling distance) at a 41 m flying height.

scholarly journals Measuring Height Characteristics of Sagebrush (Artemisia sp.) Using Imagery Derived from Small Unmanned Aerial Systems (sUAS)

Drones ◽  
2020 ◽  
Vol 4 (1) ◽  
pp. 6 ◽  
Author(s):  
Ryan G. Howell ◽  
Ryan R. Jensen ◽  
Steven L. Petersen ◽  
Randy T. Larsen
Keyword(s):  
Data Storage ◽  
Accuracy Assessment ◽  
Point Clouds ◽  
Unmanned Aerial Systems ◽  
Surface Model ◽  
Sage Grouse ◽  
Double Pass ◽  
Single Pass ◽  
3D Point Clouds ◽  
Aerial Systems

In situ measurements of sagebrush have traditionally been expensive and time consuming. Currently, improvements in small Unmanned Aerial Systems (sUAS) technology can be used to quantify sagebrush morphology and community structure with high resolution imagery on western rangelands, especially in sensitive habitat of the Greater sage-grouse (Centrocercus urophasianus). The emergence of photogrammetry algorithms to generate 3D point clouds from true color imagery can potentially increase the efficiency and accuracy of measuring shrub height in sage-grouse habitat. Our objective was to determine optimal parameters for measuring sagebrush height including flight altitude, single- vs. double- pass, and continuous vs. pause features. We acquired imagery using a DJI Mavic Pro 2 multi-rotor Unmanned Aerial Vehicle (UAV) equipped with an RGB camera, flown at 30.5, 45, 75, and 120 m and implementing single-pass and double-pass methods, using continuous flight and paused flight for each photo method. We generated a Digital Surface Model (DSM) from which we derived plant height, and then performed an accuracy assessment using on the ground measurements taken at the time of flight. We found high correlation between field measured heights and estimated heights, with a mean difference of approximately 10 cm (SE = 0.4 cm) and little variability in accuracy between flights with different heights and other parameters after statistical correction using linear regression. We conclude that higher altitude flights using a single-pass method are optimal to measure sagebrush height due to lower requirements in data storage and processing time.

scholarly journals POTENTIAL OF FULL WAVEFORM AIRBORNE LASER SCANNING DATA FOR URBAN AREA CLASSIFICATION – TRANSFER OF CLASSIFICATION APPROACHES BETWEEN MISSIONS

2015 ◽  
Vol XL-7/W3 ◽  
pp. 1317-1323 ◽  
Author(s):  
G. Tran ◽  
D. Nguyen ◽  
M. Milenkovic ◽  
N. Pfeifer
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Surface Model ◽  
Knowledge Based ◽  
Full Waveform ◽  
Airborne Laser ◽  
Point Density ◽  
Area Classification

Full-waveform (FWF) LiDAR (Light Detection and Ranging) systems have their advantage in recording the entire backscattered signal of each emitted laser pulse compared to conventional airborne discrete-return laser scanner systems. The FWF systems can provide point clouds which contain extra attributes like amplitude and echo width, etc. In this study, a FWF data collected in 2010 for Eisenstadt, a city in the eastern part of Austria was used to classify four main classes: buildings, trees, waterbody and ground by employing a decision tree. Point density, echo ratio, echo width, normalised digital surface model and point cloud roughness are the main inputs for classification. The accuracy of the final results, correctness and completeness measures, were assessed by comparison of the classified output to a knowledge-based labelling of the points. Completeness and correctness between 90% and 97% was reached, depending on the class. While such results and methods were presented before, we are investigating additionally the transferability of the classification method (features, thresholds …) to another urban FWF lidar point cloud. Our conclusions are that from the features used, only echo width requires new thresholds. A data-driven adaptation of thresholds is suggested.

scholarly journals Accuracy assessment of building point clouds automatically generated from iphone images

2014 ◽  
Vol XL-5 ◽  
pp. 547-552 ◽  
Author(s):  
B. Sirmacek ◽  
R. Lindenbergh
Keyword(s):  
Measurement Errors ◽  
Point Cloud ◽  
Model Updating ◽  
Accuracy Assessment ◽  
Low Cost ◽  
Point Clouds ◽  
3D Models ◽  
Map Updating ◽  
Cloud Points ◽  
Point To Point

Low-cost sensor generated 3D models can be useful for quick 3D urban model updating, yet the quality of the models is questionable. In this article, we evaluate the reliability of an automatic point cloud generation method using multi-view iPhone images or an iPhone video file as an input. We register such automatically generated point cloud on a TLS point cloud of the same object to discuss accuracy, advantages and limitations of the iPhone generated point clouds. For the chosen example showcase, we have classified 1.23% of the iPhone point cloud points as outliers, and calculated the mean of the point to point distances to the TLS point cloud as 0.11 m. Since a TLS point cloud might also include measurement errors and noise, we computed local noise values for the point clouds from both sources. Mean (μ) and standard deviation (&amp;sigma;) of roughness histograms are calculated as (μ<sub>1</sub> = 0.44 m., &amp;sigma;<sub>1</sub> = 0.071 m.) and (μ<sub>2</sub> = 0.025 m., &amp;sigma;<sub>2</sub> = 0.037 m.) for the iPhone and TLS point clouds respectively. Our experimental results indicate possible usage of the proposed automatic 3D model generation framework for 3D urban map updating, fusion and detail enhancing, quick and real-time change detection purposes. However, further insights should be obtained first on the circumstances that are needed to guarantee a successful point cloud generation from smartphone images.

scholarly journals Automated 3D-Objectdocumentation on the Base of an Image Set

2011 ◽  
Vol 6 ◽  
pp. 370-375
Author(s):  
Sebastian Vetter ◽  
Gunnar Siedler
Keyword(s):  
Point Cloud ◽  
Search Algorithm ◽  
Three Dimensional ◽  
Point Clouds ◽  
Spatial Dimension ◽  
High Accuracy ◽  
Relative Orientation ◽  
Reference Points ◽  
Surface Model ◽  
Object Surface

Digital stereo-photogrammetry allows users an automatic evaluation of the spatial dimension and the surface texture of objects. The integration of image analysis techniques simplifies the automation of evaluation of large image sets and offers a high accuracy [1]. Due to the substantial similarities of stereoscopic image pairs, correlation techniques provide measurements of subpixel precision for corresponding image points. With the help of an automated point search algorithm in image sets identical points are used to associate pairs of images to stereo models and group them. The found identical points in all images are basis for calculation of the relative orientation of each stereo model as well as defining the relation of neighboured stereo models. By using proper filter strategies incorrect points are removed and the relative orientation of the stereo model can be made automatically. With the help of 3D-reference points or distances at the object or a defined distance of camera basis the stereo model is orientated absolute. An adapted expansion- and matching algorithm offers the possibility to scan the object surface automatically. The result is a three dimensional point cloud; the scan resolution depends on image quality. With the integration of the iterative closest point- algorithm (ICP) these partial point clouds are fitted to a total point cloud. In this way, 3D-reference points are not necessary. With the help of the implemented triangulation algorithm a digital surface models (DSM) can be created. The texturing can be made automatically by the usage of the images that were used for scanning the object surface. It is possible to texture the surface model directly or to generate orthophotos automatically. By using of calibrated digital SLR cameras with full frame sensor a high accuracy can be reached. A big advantage is the possibility to control the accuracy and quality of the 3d-objectdocumentation with the resolution of the images. The procedure described here is implemented in software Metigo 3D.

scholarly journals Can 3D Point Clouds Replace GCPs?

2014 ◽  
Vol II-5 ◽  
pp. 347-354 ◽  
Author(s):  
G. Stavropoulou ◽  
G. Tzovla ◽  
A. Georgopoulos
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Large Scale ◽  
Point Clouds ◽  
Field Work ◽  
Surface Model ◽  
Control Points ◽  
3D Point Clouds ◽  
Surface Models

Over the past decade, large-scale photogrammetric products have been extensively used for the geometric documentation of cultural heritage monuments, as they combine metric information with the qualities of an image document. Additionally, the rising technology of terrestrial laser scanning has enabled the easier and faster production of accurate digital surface models (DSM), which have in turn contributed to the documentation of heavily textured monuments. However, due to the required accuracy of control points, the photogrammetric methods are always applied in combination with surveying measurements and hence are dependent on them. Along this line of thought, this paper explores the possibility of limiting the surveying measurements and the field work necessary for the production of large-scale photogrammetric products and proposes an alternative method on the basis of which the necessary control points instead of being measured with surveying procedures are chosen from a dense and accurate point cloud. Using this point cloud also as a surface model, the only field work necessary is the scanning of the object and image acquisition, which need not be subject to strict planning. To evaluate the proposed method an algorithm and the complementary interface were produced that allow the parallel manipulation of 3D point clouds and images and through which single image procedures take place. The paper concludes by presenting the results of a case study in the ancient temple of Hephaestus in Athens and by providing a set of guidelines for implementing effectively the method.

scholarly journals Physical Modelling of Blue Mussel Dropper Lines for the Development of Surrogates and Hydrodynamic Coefficients

2019 ◽  
Vol 7 (3) ◽  
pp. 65 ◽  
Author(s):  
Jannis Landmann ◽  
Thorsten Ongsiek ◽  
Nils Goseberg ◽  
Kevin Heasman ◽  
Bela Buck ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Blue Mussel ◽  
Physical Modelling ◽  
Point Clouds ◽  
Surface Model ◽  
Test Duration ◽  
Blue Mussels ◽  
Hydrodynamic Behaviour ◽  
Custom Made

In this work, laboratory tests with live bivalves as well as the conceptual design of additively manufactured surrogate models are presented. The overall task of this work is to develop a surrogate best fitting to the live mussels tested in accordance to the identified surface descriptor, i.e., the Abbott–Firestone Curve, and to the hydrodynamic behaviour by means of drag and inertia coefficients. To date, very few investigations have focused on loads from currents as well as waves. Therefore, tests with a towing carriage were carried out in a wave flume. A custom-made rack using mounting clamps was built to facilitate carriage-run tests with minimal delays. Blue mussels (Mytilus edulis) extracted from a site in Germany, which were kept in aerated seawater to ensure their survival for the test duration, were used. A set of preliminary results showed drag and inertia coefficients C D and C M ranging from 1.16–3.03 and 0.25 to 1.25. To derive geometrical models of the mussel dropper lines, 3-D point clouds were prepared by means of 3-D laser scanning to obtain a realistic surface model. Centered on the 3-D point cloud, a suitable descriptor for the mass distribution over the surface was identified and three 3-D printed surrogates of the blue mussel were developed for further testing. These were evaluated regarding their fit to the original 3-D point cloud of the live blue mussels via the chosen surface descriptor.

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