3,785 research outputs found
MIMIC : Mobile Mapping Point Density Calculator
The current generation of Mobile Mapping Systems (MMSs) capture increasingly larger amounts of data in a short time frame. Due to the relative novelty of this technology there is no concrete understanding of the point density that differ- ent hardware configurations and operating parameters will exhibit on objects at specific distances. Depending on the project requirements, obtaining the required point density impacts on survey time, processing time, data storage and is the underlying limit of automated algorithms. A limited un- derstanding of the capabilities of these systems means that defining point density in project specifications is a compli- cated process. We are in the process of developing a method for determining the quantitative resolution of point clouds collected by a MMS with respect to known objects at spec- ified distances. We have previously demonstrated the capa- bilities of our system for calculating point spacing, profile angle and profile spacing individually. Each of these ele- ments are a major factor in calculating point density on arbitrary objects, such as road signs, poles or buildings - all important features in asset management surveys. This paper will introduce the current version of the MobIle Map- ping point densIty Calculator (MIMIC), MIMIC’s visuali- sation module and finally discuss the methods employed to validate our work
Calculating and Assessing Mobile Mapping System Point Density for Roadside Infrastructure Surveys
The current generation of Mobile Mapping Systems (MMSs) capture increasingly
larger amounts of data in a short time frame. Due to the relative novelty
of this technology there is no concrete understanding of the point density that
different scanner confgurations and scanner hardware settings will exhibit
on objects at specific distances. Depending on the project requirements, obtaining
the required point density impacts on survey time, processing time,
data storage and is the underlying limit of automated algorithms. Insufficient knowledge of the factors in
uencing MMS point density means that
defning point density in project specifications is a complicated process. The
objectives of this thesis are to calculate point density, to assess MMS laser
scanner configuration and hardware settings and to benchmark a selection
of MMSs in terms of their point density. The calculation methods involve
a combination of algorithms applying 3D surface normals and 2D geometric
formulae and outputs profile angle, profile spacing, point spacing and
point density. Each of these elements are a major factor in calculating point
density on arbitrary objects, such as road signs, poles or buildings - all important
features in asset management surveys. These algorithms are combined
in a system called the Mobile Mapping Point Density Calculator (MIMIC).
MIMIC is then applied in a series of tests identifying the recommended MMS
laser scanner configuration and scanner hardware settings for near side infrastructure.
The in
uence that the scanner orientation and location on the
MMS has on point density is quantified, resulting in a recommended MMS
laser scanner configuration. A series of benchmarking tests assess the performance
of one commercial and two theoretical MMSs in terms of their point
density. The recommended configuration identified in the previous tests allows a low specification MMS to increase its performance in relation to a
higher specification MMS. The benchmarking tests also highlight that a high
pulse repetition rate is preferable to a high mirror frequency for maximising
point density. The findings in this thesis enable a MMS to be configured to
maximise point density for specific targets. Researchers can utilise MIMIC
to tailor their automated algorithm's point density requirements for specific
targets
RUR53: an Unmanned Ground Vehicle for Navigation, Recognition and Manipulation
This paper proposes RUR53: an Unmanned Ground Vehicle able to autonomously
navigate through, identify, and reach areas of interest; and there recognize,
localize, and manipulate work tools to perform complex manipulation tasks. The
proposed contribution includes a modular software architecture where each
module solves specific sub-tasks and that can be easily enlarged to satisfy new
requirements. Included indoor and outdoor tests demonstrate the capability of
the proposed system to autonomously detect a target object (a panel) and
precisely dock in front of it while avoiding obstacles. They show it can
autonomously recognize and manipulate target work tools (i.e., wrenches and
valve stems) to accomplish complex tasks (i.e., use a wrench to rotate a valve
stem). A specific case study is described where the proposed modular
architecture lets easy switch to a semi-teleoperated mode. The paper
exhaustively describes description of both the hardware and software setup of
RUR53, its performance when tests at the 2017 Mohamed Bin Zayed International
Robotics Challenge, and the lessons we learned when participating at this
competition, where we ranked third in the Gran Challenge in collaboration with
the Czech Technical University in Prague, the University of Pennsylvania, and
the University of Lincoln (UK).Comment: This article has been accepted for publication in Advanced Robotics,
published by Taylor & Franci
Implementation and assessment of two density-based outlier detection methods over large spatial point clouds
Several technologies provide datasets consisting of a large number of spatial points, commonly referred to as point-clouds. These point datasets provide spatial information regarding the phenomenon that is to be investigated, adding value through knowledge of forms and spatial relationships. Accurate methods for automatic outlier detection is a key step. In this note we use a completely open-source workflow to assess two outlier detection methods, statistical outlier removal (SOR) filter and local outlier factor (LOF) filter. The latter was implemented ex-novo for this work using the Point Cloud Library (PCL) environment. Source code is available in a GitHub repository for inclusion in PCL builds. Two very different spatial point datasets are used for accuracy assessment. One is obtained from dense image matching of a photogrammetric survey (SfM) and the other from floating car data (FCD) coming from a smart-city mobility framework providing a position every second of two public transportation bus tracks. Outliers were simulated in the SfM dataset, and manually detected and selected in the FCD dataset. Simulation in SfM was carried out in order to create a controlled set with two classes of outliers: clustered points (up to 30 points per cluster) and isolated points, in both cases at random distances from the other points. Optimal number of nearest neighbours (KNN) and optimal thresholds of SOR and LOF values were defined using area under the curve (AUC) of the receiver operating characteristic (ROC) curve. Absolute differences from median values of LOF and SOR (defined as LOF2 and SOR2) were also tested as metrics for detecting outliers, and optimal thresholds defined through AUC of ROC curves. Results show a strong dependency on the point distribution in the dataset and in the local density fluctuations. In SfM dataset the LOF2 and SOR2 methods performed best, with an optimal KNN value of 60; LOF2 approach gave a slightly better result if considering clustered outliers (true positive rate: LOF2\u2009=\u200959.7% SOR2\u2009=\u200953%). For FCD, SOR with low KNN values performed better for one of the two bus tracks, and LOF with high KNN values for the other; these differences are due to very different local point density. We conclude that choice of outlier detection algorithm very much depends on characteristic of the dataset\u2019s point distribution, no one-solution-fits-all. Conclusions provide some information of what characteristics of the datasets can help to choose the optimal method and KNN values
High-precision 3D object capturing with static and kinematic terrestrial laser scanning in industrial applications-approaches of quality assessment
Abstract Terrestrial laser scanning is used in many disciplines of engineering. Examples include mobile mapping, architecture surveying, archaeology, as well as monitoring and surveillance measurements. For most of the mentioned applications, 3D object capturing in an accuracy range of several millimeters up to a few centimeters is sufficient. However, in engineering geodesy, particularly in industrial surveying or monitoring measurements, accuracies in a range of a few millimeters are required. Additional increased quality requirements apply to these applications. This paper focuses on the quality investigation of data captured with static and kinematic terrestrial laser scanning. For this purpose, suitable sensors, which are typically used in the approach of a multi-sensor-system, as well as the corresponding data capturing/acquisition strategies, are presented. The aim of such systems is a geometry- and surface-based analysis in an industrial environment with an accuracy of +/- 1-2 mm or better
Localization in Unstructured Environments: Towards Autonomous Robots in Forests with Delaunay Triangulation
Autonomous harvesting and transportation is a long-term goal of the forest
industry. One of the main challenges is the accurate localization of both
vehicles and trees in a forest. Forests are unstructured environments where it
is difficult to find a group of significant landmarks for current fast
feature-based place recognition algorithms. This paper proposes a novel
approach where local observations are matched to a general tree map using the
Delaunay triangularization as the representation format. Instead of point cloud
based matching methods, we utilize a topology-based method. First, tree trunk
positions are registered at a prior run done by a forest harvester. Second, the
resulting map is Delaunay triangularized. Third, a local submap of the
autonomous robot is registered, triangularized and matched using triangular
similarity maximization to estimate the position of the robot. We test our
method on a dataset accumulated from a forestry site at Lieksa, Finland. A
total length of 2100\,m of harvester path was recorded by an industrial
harvester with a 3D laser scanner and a geolocation unit fixed to the frame.
Our experiments show a 12\,cm s.t.d. in the location accuracy and with
real-time data processing for speeds not exceeding 0.5\,m/s. The accuracy and
speed limit is realistic during forest operations
Pyörivien monilaserkeilainjärjestelmien geometrinen kalibrointi
The introduction of light-weight and low-cost multi-beam laser scanners provides ample opportunities in positioning and mapping as well as automation and robotics. The fields of view (FOV) of these sensors can be further expanded by actuation, for example by rotation. These rotating multi-beam lidar (RMBL) systems can provide fast and expansive coverage of the geometries of spaces, but the nature of the sensors and their actuation leave room for improvement in accuracy and precision. Geometric calibration methods addressing this space have been proposed, and this thesis reviews a selection of these methods and evaluates their performance when applied to a set of data samples collected using a custom RMBL platform and six Velodyne multi-beam sensors (one VLP-16 Lite, four VLP-16s and one VLP-32C). The calibration algorithms under inspection are unsupervised and data-based, and they are quantitatively compared to a target-based calibration performed using a high-accuracy point cloud obtained using a terrestrial laser scanner as a reference. The data-based calibration methods are automatic plane detection and fitting, a method based on local planarity and a method based on the information-theoretic concept of information entropy. It is found that of these, the plane-fitting and entropy-based measures for point cloud quality obtain the best calibration results.Kevyet ja edulliset monilaserkeilaimet tuovat uusia mahdollisuuksia paikannus- ja kartoitusaloille mutta myös automaatioon ja robotiikkaan. Näiden sensorien näköaloja voidaan kasvattaa entisestään esimerkiksi pyörittämällä, ja näin toteutettavat pyörivät monilaserkeilainjärjestelmät tuottavat nopeasti kattavaa geometriaa niitä ympäröivistä tiloista. Sensorien rakenne ja järjestelmän liikkuvuus lisäävät kuitenkin kohinaa ja epävarmuutta mittauksissa, minkä vuoksi erilaisia geometrisia kalibrointimenetelmiä onkin ehdotettu aiemmassa tutkimuksessa. Tässä diplomityössä esitellään valikoituja kalibrointimenetelmiä ja arvioidaan niiden tuloksia koeasetelmassa, jossa pyörivälle alustalle asennetuilla Velodyne-monilaserkeilaimilla (yksi VLP-16 Lite, neljä VLP-16:aa ja yksi VLP-32C) mitataan liikuntasalin geometriaa. Tarkasteltavat menetelmät ovat valvomattomia ja vain mittauksiin perustuvia ja niitä verrataan samasta tilasta hankittuun tarkkaan maalaserkeilausaineistoon. Menetelmiä ovat tasojen automaattinen etsintä ja sovitus, paikalliseen tasomaisuuteen perustuva menetelmä sekä informaatioteoreettiseen entropiaan perustuva menetelmä. Näistä tasojen sovitus ja entropiamenetelmä saavuttivat parhaat kalibrointitulokset referenssikalibraatioon verrattaessa
Yhtäaikainen paikannus ja puuston kartoitus 2D- ja 3D- laserskannereilla
Yhtäaikainen paikannus ja puuston kartoitus 2D- ja 3D- laserskannereilla esittää tavan mitata ja kartoittaa metsän puut. Työssä mittaukset tehtiin paikallisesti metsässä käyttäen kaksi ja kolmiulotteisia laserskannereita liikkeestä mitaten. Työ esittää menetelmän puumaiset kohteiden tunnistamiseen mittalaitteiden tuottamasta pisteparvesta reaaliajassa. Työssä mittaus ja kartoitusalgoritmit sovitetaan erityistesti metsän kartoitusta ja puuston mittausta varten.
Diplomityössä yhtäaikaisen paikoituksen ja kartoituksen ongelmaa lähdetään ratkaisemaan mittauslaitteen mahdollisimman tarkan paikan ja asennon estimoinnista metsäolosuhteissa. Mittauslaitteiston paikkaa mitataan laserodometrialla, jossa perättäisiä laserkeilauksia verrataan toisiinsa ja näiden väliltä tunnistetaan liike suhteessa ympäristöön. Työssä kehitetään uusia heuristisia paikannusmetodeja metsäympäristöön. Työ esittelee uuden tavan käyttää ristikorrelaatioita laserodometriassa ja näyttää miten gyro- ja kiihtyvyysantureita voidaan käyttää odometriatiedon parantamiseen.
Diplomityö esittää piirrepohjaisen puukartan, jonne 2D- ja 3D-laseretäisyysmittauksista lasketut piirteet lisätään. Karttaa päivitetään jatkuvasti ja uusinta kartan tietoa käytetään mitatun paikkatiedon parantamiseen. Samoin kaikkia kerättyjä mittauksia käytetään tilastollisesti parantamaan aikaisemmin eri korkeuksilta laskettuja puurunkojen läpimittaestimaatteja.
Lopputuloksena saatu kartta on melko tarkka. Kartoituksessa puun läpimitan tarkkuus on muutamia senttimetrejä ja puun paikan tarkkuus muuta kymmenen senttimetriä. Mittauslaitteen paikan arvioidaan olevan tarkempi kuin puiden, koska suurta määrää kartoitettuja puita käytetään mittauslaitteen paikan ja asennon sovittamiseen. Suhteellinen kartta on kiinnitetty globaaliin koordinaatistoon GPS mittalaitteen avulla
Equine body weight estimation using three-dimensional images
Includes bibliographical references.2015 Summer.Accurately estimating the body weight (BW) of a horse is important in order to make appropriate management and treatment decisions. Most field equine veterinarians and experienced equine people, however, visually estimate BW because large animal scales are impractical for field use due to the weight (>80 kg), size (length >200 cm), and cost (>$1,000). There are some alternative BW estimation methods such as a weight tape or BW estimation using a combination of heart girth and body length measurements. These methods, however, have 5 - 15% or even higher margin of error. According to human studies, there is a high correlation between BW and body volume (BV). Correlation coefficient (R) between these two variables is 0.996-0.998. Our study was designed to develop methods to estimate the BW of horses by using 3D image based BV measurement. 3D imaging technology allows easy and accurate measurement of diverse indices of an object, including the volume. Recent development of Structure-light 3D scanning technology allows 3D scanning of an object as large as 3 by 3 square meter in a short time. In this study, 3D images of 22 and 11 horses were obtained by using 3D scanning (3DScan) and photogrammetry (2Dto3D), respectively. BV and trunk volume (TV) of the horses were measured from the obtained 3D images. Measurements of BW using five conventional methods (visual estimation, 2 weight tapes (Purina, Shell), estimated BW by using heart girth and body length (Carroll’s formula), and a large animal scale) were also conducted, and the data of body condition score (BCS), sex, coat color, and coat type of the horses were collected. Linear regression models to estimate the BW of the horse based on the volume and other independent variables were developed using regression model stepwise selection procedures (P<0.05). Variables selected in 3DScan method were BV, sex, and coat type, and, in 2Dto3D method, BV (TV) was selected. The coefficient of determination of the developed regression models were 0.95 and 0.78-0.82, respectively, and the average percent errors of the predicted BW compared to the true BW of horses were 2.07 % and 2.67 %, respectively. The accuracy of the 3DScan method was significantly more accurate than WT, Carroll’s formual, and VE (P<0.05). 3D image based BW measurement method had higher accuracy and convenience compared to conventional alternative BW measuring methods. Accurate and easy determination of BW using 3D images will allow for regular BW measurement in the field and allow optimal equine health management by equine stakeholders and practitioners. The 3D images obtained in this study were highly detailed. Further graphical analysis of the obtained 3D images will make it possible to use this technology on automatic evaluation of body condition score, equine conformation evaluation, breed registration, and the study of pharmacokinetics and dynamics of newly developed drugs. This research findings may also have utility for application to wild or zoo animals such as the elephant, rhinoceros, or even the tiger where hands on collection of body weight would be challenging
Assessment of handheld mobile terrestrial laser scanning for estimating tree parameters
Sustainable forest management heavily relies on the accurate estimation of tree parameters. Among others, the diameter at breast height (DBH) is important for extracting the volume and mass of an individual tree. For systematically estimating the volume of entire plots, airborne laser scanning (ALS) data are used. The estimation model is frequently calibrated using manual DBH measurements or static terrestrial laser scans (STLS) of sample plots. Although reliable, this method is time-consuming, which greatly hampers its use. Here, a handheld mobile terrestrial laser scanning (HMTLS) was demonstrated to be a useful alternative technique to precisely and efficiently calculate DBH. Different data acquisition techniques were applied at a sample plot, then the resulting parameters were comparatively analysed. The calculated DBH values were comparable to the manual measurements for HMTLS, STLS, and ALS data sets. Given the comparability of the extracted parameters, with a reduced point density of HTMLS compared to STLS data, and the reasonable increase of performance, with a reduction of acquisition time with a factor of 5 compared to conventional STLS techniques and a factor of 3 compared to manual measurements, HMTLS is considered a useful alternative technique
- …