12 research outputs found

    EFFECTIVE NAVIGATION AND MAPPING OF A CLUTTERED ENVIRONMENT USING A MOBILE ROBOT

    Get PDF
    Today, the as-is three-dimensional point cloud acquisition process for understanding scenes of interest, monitoring construction progress, and detecting safety hazards uses a laser scanning system mounted on mobile robots, which enables it faster and more automated, but there is still room for improvement. The main disadvantage of data collection using laser scanners is that point cloud data is only collected in a scanner’s line of sight, so regions in three-dimensional space that are occluded by objects are not observable. To solve this problem and obtain a complete reconstruction of sites without information loss, scans must be taken from multiple viewpoints. This thesis describes how such a solution can be integrated into a fully autonomous mobile robot capable of generating a high-resolution three-dimensional point cloud of a cluttered and unknown environment without a prior map. First, the mobile platform estimates unevenness of terrain and surrounding environment. Second, it finds the occluded region in the currently built map and determines the effective next scan location. Then, it moves to that location by using grid-based path planner and unevenness estimation results. Finally, it performs the high-resolution scanning that area to fill out the point cloud map. This process repeats until the designated scan region filled up with scanned point cloud. The mobile platform also keeps scanning for navigation and obstacle avoidance purposes, calculates its relative location, and builds the surrounding map while moving and scanning, a process known as simultaneous localization and mapping. The proposed approaches and the system were tested and validated in an outdoor construction site and a simulated disaster environment with promising results.Ph.D

    Система керування колісною платформою мобільного робота сканування місцевості

    Get PDF
    Пояснювальна записка магістерської дисертації складається з п'яти розділів, містить 35 таблиць, 8 додатків та 32 джерел – загалом 115 сторінок. Об`єкт дослідження: процес керування мобільним роботом. Мета дослідження: підвищити якість управління мобільним роботом завдяки розробці програмного додатку та сучасних апаратних рішень. У першому розділі розглядається загальний огляд та класифікація типів керування мобільними роботами, описується структура та аналізуються існуючі типи пересування робототехнічних систем. У другому розділі надається загальна структура розробленої системи та її компонентів. У третьому розділі описується апаратна інфраструктура системи. У четвертому розділі розглядається розробка програмного додатку системи керування мобільним роботом. У рамках п’ятого розділу було проведено маркетинговий аналіз стартап-проєкту.The explanatory note of the diploma project consists of five sections, contains 35 table, 8 applications and 16 sources - a total of 115 pages. The object of study: mobile robot control process. The aim of the study: improve the quality of mobile robot control through the development of software applications and modern hardware solutions. The first section considers the general overview and classification of types of control of mobile robots, describes the structure and analyzes the existing types of movement of robotic systems. The second section provides a general structure of the developed system and its components. The third section describes the hardware infrastructure of the system. The fourth section discusses the development of a software application for a mobile robot control system. In the fifth section, a marketing analysis of the startup project was conducted

    Designing and Evaluating Next-Generation Thermographic Systems to Support Residential Energy Audits

    Get PDF
    Buildings account for 41% of primary energy consumption in the United States—more than any other sector—and contribute to an increasing portion of carbon dioxide emissions (33% in 1980 vs. 40% in 2009). To help address this problem, the U.S. Department of Energy recommends conducting energy audits to identify sources of inefficiencies that contribute to rising energy use. One effective technique used during energy audits is thermography. Thermographic-based energy auditing activities involve the use of thermal cameras to identify, diagnose, and document energy efficiency issues in the built environment that are visible as anomalous patterns of electromagnetic radiation. These patterns may indicate locations of air leakages, areas of missing insulation, or moisture issues in the built environment. Sensor improvements and falling costs have increased the popularity of this auditing technique, but its effectiveness is often mediated by the training and experience of the auditor. Moreover, given the increasing availability of commodity thermal cameras and the potential for pervasive thermographic scanning in the built environment, there is a surprising lack of understanding about people’s perceptions of this sensing technology and the challenges encountered by an increasingly diverse population of end-users. Finally, there are few specialized tools and methods to support the auditing activities of end-users. To help address these issues, my work focuses on three areas: (i) formative studies to understand and characterize current building thermography practices, benefits, and challenges, (ii) human-centered explorations into the role of automation and the potential of pervasive thermographic scanning in the built environment, and (iii) evaluations of novel, interactive building thermography systems. This dissertation presents a set of studies that qualitatively characterizes building thermography practitioners, explores prototypes of novel thermographic systems at varying fidelity, and synthesizes findings from several field deployments. This dissertation contributes to the fields of sustainability, computer science, and HCI through: (i) characterizations of the end-users of thermography, (ii) critical feedback on proposed automated thermographic solutions, (iii) the design and evaluation of a novel longitudinal thermography system designed to augment the data collection and analysis activities of end-users, and (iv) design recommendations for future thermographic systems

    MULTI-CAMERA SYSTEM CALIBRATION OF A LOW-COST REMOTELY OPERATED VEHICLE FOR UNDERWATER CAVE EXPLORATION

    Get PDF
    Exploration, documentation and mapping of underwater environment is one of the biggest open challenges for science and engineering. Humankind is not naturally designed to operate in water and, despite the enormous technological advancement that offers nowadays unprecedented opportunities, diving and working underwater is still very dangerous, especially in confined spaces such as underwater caves. Great research efforts are currently devoted to underwater autonomous navigation, but available solutions still mainly rely on complex and expensive systems, due to the difficulty of adapting localization and mapping sensors and algorithms suited for terrestrial or aerial applications. However, small and affordable underwater remotely operated vehicles (ROVs) are available, which offer good opportunities for underwater exploration and mapping. This paper focuses on the development of a small, low-cost ROV designed for 3D mapping of underwater environments, like caves. The system is based on a commercially available vehicle, the BluRov2, and relies on the use of up to 12 action cameras (GoPro) mounted on it. A trifocal camera system for underwater real-time visual odometry can also be included. The work describes the photogrammetric procedure developed for the synchronization and calibration of the GoPro cameras and provides a thorough analysis on the achievable results

    3D Thermal Modeling of Built Environments Using Visual and Infrared Sensing

    Get PDF
    Infrared thermography (IR) is a modern, non-destructive evaluation technology for monitoring and assessing built environments. It mainly relies on measuring surface temperature to identify any potential defects or damages. Currently, IR has been introduced widely in applications such as facility condition assessment and energy performance analysis of existing buildings. However, most of the current practices in IR rely only on 2D thermal images which are time-consuming and labor-intensive. On the other hand, the rapid improvement of high-defined IR cameras has become a powerful tool in infrared sensing. Accordingly, this has facilitated its implementation in 3D thermal modeling techniques to replace the current 2D approach in thermal inspection and building energy efficiency. Yet, further studies need to be performed to overcome 3D thermal modeling limitations such as the high cost, slow process, and the need of highly trained professionals. The main objectives of this research are to (a) test the potentiality of using 2D visible and thermal images which were collected separately through digital and infrared cameras respectively, for the 3D thermal modeling of built environments, and (b) investigate the efficiency of the proposed methodology by comparing it to a developed experimental design in terms of evaluating density, time, and cost. In specific, the visible images were used in modeling 3D point clouds by applying the structure from motion (sfm) approach. In parallel, the overlapping thermal images were stitched to form a thermal panoramic image that covers a large surface area with an accurate temperature representation. The stitched thermal images were then mapped to the reconstructed 3D point cloud in order to generate both thermal and metric measurements of built environments. Correspondingly, the output was compared to another 3D thermal point clouds which were developed by a laser scanner and an infrared camera. The comparison was conducted by means of evaluating density, time, and cost. Finally, the comparison results of three different built environments in the city of Montreal, Canada; demonstrate that 3D thermal modeling using separate 2D thermal and visible images was able to generate a dense geometric and thermal information of built environments. Also, this approach is affordable in terms of cost and time

    Point clouds and thermal data fusion for automated gbXML-based building geometry model generation

    Get PDF
    Existing residential and small commercial buildings now represent the greatest opportunity to improve building energy efficiency. Building energy simulation analysis is becoming increasingly important because the analysis results can assist the decision makers to make decisions on improving building energy efficiency and reducing environmental impacts. However, manually measuring as-is conditions of building envelops including geometry and thermal value is still a labor-intensive, costly, and slow process. Thus, the primary objective of this research was to automatically collect and extract the as-is geometry and thermal data of the building envelope components and create a gbXML-based building geometry model. In the proposed methodology, a rapid and low-cost data collection hardware system was designed by integrating 3D laser scanners and an infrared (IR) camera. Secondly, several algorithms were created to automatically recognize various components of building envelope as objects from collected raw data. The extracted 3D semantic geometric model was then automatically saved as an industry standard file format for data interoperability. The feasibility of the proposed method was validated through three case studies. The contributions of this research include 1) a customized low-cost hybrid data collection system development to fuse various data into a thermal point cloud; 2) an automatic method of extracting building envelope components and its geometry data to generate gbXML-based building geometry model. The broader impacts of this research are that it could offer a new way to collect as is building data without impeding occupants’ daily life, and provide an easier way for laypeople to understand the energy performance of their buildings via 3D thermal point cloud visualization.Ph.D

    Methods for improving geometric quality of mobile laser scanning data in forest environment

    Get PDF
    Liikkuvalla laserkeilauksella voidaan mitata sellaisessa ympäristössä, jota muut laserkeilaimet eivät tavoita tai jossa niiden käyttäminen on hidasta. Ilmalaserkeilaus ei tavoita puiden runkoja ja maalaserkeilain on hankala sekä hidas liikuttaa. Laserkeilaimella mitattujen kohteiden pisteiden sijainnit saadaan tallennettua oikein vain, jos laserkeilaimen sijainti mittaushetkellä tunnetaan oikein. Liikkuvassa kartoituksessa laserkeilaimen sijainti määritetään GNSS-satelliittipaikannuksen (Global Navigation Satellite System) ja inertiapaikannuksen yhdistelmän avulla. MLS-keilaimen kulkema laskennallinen reitti eli trajektori tuotetaan GNSS-IMU-paikannuslaitteiston tallentamien havaintojen avulla. Metsäympäristössä GNSS-yhteys satelliitteihin saattaa kadota ajoittain eikä pelkkä inertiapaikannus pysty pitämään paikannustarkkuutta riittävän hyvänä pitkiä aikoja. Tällöin pisteiden sijainti suhteessa todel-liseen sijaintiin vääristyy eli geometrinen laatu heikkenee. MLS-aineiston geometrian parantamiseksi trajektorista etsittiin tutkimuksessa leikkauskoh-tia. Leikkauskohdat olivat kohtia, joissa sama kohta maastosta oli mitattu ainakin kahtena eri ajanhetkenä. Tällaisista leikkauskohdista muodostettiin samasta alueesta kaksi erilaista pistejoukkoa. Leikkauskohtien pistejoukkoja sovitettiin yhteen. Näin pistejoukot siirtyivät lähemmäs toisiaan ja niiden keskinäinen sijaintivirhe saatiin pienennettyä. Tutkimuksen koe-aineistona käytettiin liikkuvalla laserkeilaimella Akhka R2:lla mitattuja pistepilviaineistoja ja trajektoria. Vertausaineistona käytettiin maalaserkeilaimella mitattua pistepilviaineistoa samalta alueelta. Yhteensovittaminen toteutettiin Matlab-ohjelmalla. Pistepilvien käsittely ja tulosten vertailu tehtiin TerraScan-ohjelmalla. Tutkimuksessa havaittiin, että aineistojen geometrista laatua metsäympäristössä voitiin parantaa yhteensovituksella N- ja korkeussuunnissa. Parannusta tapahtui metsäympäristössä sisäisesti PLS-koeaineistossa sekä suhteessa TLS-vertausaineistoon. Korkeussuuntainen parannus metsäympäristössä oli keskiarvoltaan 0,01 m TLS-vertausaineistoon nähden ja 0,12 m PLS-koeaineiston sisäisessä vertailussa. Korkeusuunnassa 71 % metsäalueen pistejoukoista parantui PLS-koeaineiston ja TLS-vertausaineiston välisessä vertailussa ja PLS-koeaineiston sisäisessä vertailussa kaikki metsäalueen pistejoukot parantuivat. N-suuntainen parannus metsäympäristössä oli keskiarvoltaan 0,01 m TLS-vertausaineistoon nähden ja 0,02 m PLS-koeaineiston sisäisessä vertailussa. Yhteensovituksen jälkeen metsäalueella N- ja korkeussuuntainen geometrinen laatu oli parempi koeaineiston sisäisessä vertailussa kuin vertausaineistoon nähden. Yhteensovitus paransi metsäympäristön lisäksi koko alueen geometrista tarkkuutta korkeussuunnassa. Heading-kiertokulmat huonontuivat kaikissa vertailuissa ja alueilla yhteensovituksen myötä. Geometrisen laadun parantaminen on tärkeää, jotta MLS-keilauksia voidaan tehdä luotettavasti metsäympäristössä.Mobile laser scanner can measure environment, where other laser scanners cannot or where they are hard to use. Airborne laser scanning can’t measure tree stems from above. It is hard and slow to move terrestrial laser scanner. If laser scanner’s position is known, only then points from objects can be saved with correct position. Position of laser scanner can be defined with combining global navigation satellite system and inertia-system in mobile mapping. Computational route of MLS is called trajectory. Trajectory can be defined with GNSS-IMU-observations. GNSS cannot be connected to satellites in forest environment all the time. Inertia-positioning cannot keep correct positioning quality for long. Then it is not possible to reliably know where points should be mapped in a real world. That means that geometric quality of the data decreases. Intersections within the trajectory were sought for improving geometric quality of MLS-point cloud. Intersections were points, where laser scanner measured at least twice in different times. It is possible to find two different set of points around those intersections for mutual matching. By matching the set of points it is possible to reduce positioning error between the set of points. The point cloud and corresponding trajectory for this study were measured by mobile laser scanner Akhka R2. Reference point cloud was measured with terrestrial laser scanner from the same area. Matlab-program was used to implement the matching method. TerraScan-program was used for processing point clouds and comparing results. The geometric quality of the point clouds was improved for forest environment in N- and elevation directions. Improvement took place in forest environment between the test and the reference point clouds. Improvement in the elevation direction was approximately 0,01 m between the test and the reference point clouds and 0,12 m inside the test point clouds in forest environment. In the elevation direction 71 % of the set of points between the test and the reference point clouds and all of the set of points inside the test point clouds were improved in forest environment. Improvement in the N-direction was approximately 0,01 m between the test and the reference point clouds and 0,02 m inside the test point clouds in forest environment. The geometric quality in N- and elevation directions was better inside the test point cloud than between the test and reference point clouds in forest environment by using the implemented matching method. The geometric quality of the whole dataset was improved in elevation direction by using the implemented matching method. The geometric quality of the whole dataset got worse in heading-angles by using the implemented matching method. It is important to improve geometric quality, so that MLS can be used reliably in the forest environment

    Koordinacija više robota za učinkovite pretraživanje prostora

    Get PDF
    This paper addresses the problem of exploration of an unknown environment by developing effective exploration strategies for a team of mobile robots equipped with continuously rotating 3D scanners. The main aim of the new strategies is to reduce the exploration time of unknown environment. Unlike most of other published works, to save time, the laser scanners rotate and scan the environment while robots are in motion. Furthermore, the new strategies are able to explore large outdoor environments as a considerable reduction of the required computations, especially those required for path planning, have been achieved. Moreover, another new exploration strategy has been developed so that robots continuously replan the order to visit the remaining unexplored areas according to the new data (i.e. updated map) collected by the robot in question or by the other team members. This new extension led to further enhancements over the above mentioned ones, but with slightly higher computational costs. Finally, to assess our new exploration strategies with different levels of environment complexity, new set of experiments were conducted in environments where obstacles are distributed according to the Hilbert curve. The results of these experiments show the effectiveness of the proposed technique to effectively distribute the robots over the environment. More importantly, we show how the optimal number of robots is related to the environment complexity.Ovaj članak istražuje problem pretraživanja nepoznatog prostora razvijanjem učinkovite strategije za tim mobilnih robota s rotirajućim 3D laserskim senzorom. Glavni cilj ove nove strategije je smanjenje vremena pretraživanja nepoznatog prostora. Za razliku od većine objavljenih radova, u ovome članku, radi smanjenja vremena, laserski senzori rotiraju i snimaju prostor dok su roboti još u pokretu. Predložene strategije, pošto se njima znatno smanjuje računska složenost, pogotovo za planiranje gibanja, omogućuju pretraživanje i vanjskih prostora prostora velikih dimenzija. Nadalje, razvijena je još jedna strategija pretraživanja koja omogućuje robotima da kontinuirano replaniraju poredak kojim će posjetiti ostatak neistraženog prostora, prema novim podacima (ažuriranoj karti) prikupljenim od njih samih ili drugih članova tima. Ovo novo proširenje nadalje unaprjeđuje performanse algoritma, ali uz nešto veću računsku složenost. Kako bi se u konačnici testirale nove strategije pretraživanja na prostorima različite složenosti, provedeni su eksperimenti s preprekama raspoređenim po Hilbertovoj krivulji. Rezultati eksperimenata pokazuju učinkovitost predložene metode u prostornom raspoređivanju robota. Od posebne je važnosti istaknuti da se u članku također istražuje odnos između broja robota i kompleksnosti prostora

    Irma3D — An Intelligent Robot for Mapping Applications*

    No full text
    corecore