18,166 research outputs found
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
A review of laser scanning for geological and geotechnical applications in underground mining
Laser scanning can provide timely assessments of mine sites despite adverse
challenges in the operational environment. Although there are several published
articles on laser scanning, there is a need to review them in the context of
underground mining applications. To this end, a holistic review of laser
scanning is presented including progress in 3D scanning systems, data
capture/processing techniques and primary applications in underground mines.
Laser scanning technology has advanced significantly in terms of mobility and
mapping, but there are constraints in coherent and consistent data collection
at certain mines due to feature deficiency, dynamics, and environmental
influences such as dust and water. Studies suggest that laser scanning has
matured over the years for change detection, clearance measurements and
structure mapping applications. However, there is scope for improvements in
lithology identification, surface parameter measurements, logistic tracking and
autonomous navigation. Laser scanning has the potential to provide real-time
solutions but the lack of infrastructure in underground mines for data
transfer, geodetic networking and processing capacity remain limiting factors.
Nevertheless, laser scanners are becoming an integral part of mine automation
thanks to their affordability, accuracy and mobility, which should support
their widespread usage in years to come
Automatic Change-based Diagnosis of Structures Using Spatiotemporal Data and As- Designed Model
abstract: Civil infrastructures undergo frequent spatial changes such as deviations between as-designed model and as-is condition, rigid body motions of the structure, and deformations of individual elements of the structure, etc. These spatial changes can occur during the design phase, the construction phase, or during the service life of a structure. Inability to accurately detect and analyze the impact of such changes may miss opportunities for early detections of pending structural integrity and stability issues. Commercial Building Information Modeling (BIM) tools could hardly track differences between as-designed and as-built conditions as they mainly focus on design changes and rely on project managers to manually update and analyze the impact of field changes on the project performance. Structural engineers collect detailed onsite data of a civil infrastructure to perform manual updates of the model for structural analysis, but such approach tends to become tedious and complicated while handling large civil infrastructures.
Previous studies started collecting detailed geometric data generated by 3D laser scanners for defect detection and geometric change analysis of structures. However, previous studies have not yet systematically examined methods for exploring the correlation between the detected geometric changes and their relation to the behaviors of the structural system. Manually checking every possible loading combination leading to the observed geometric change is tedious and sometimes error-prone. The work presented in this dissertation develops a spatial change analysis framework that utilizes spatiotemporal data collected using 3D laser scanning technology and the as-designed models of the structures to automatically detect, classify, and correlate the spatial changes of a structure. The change detection part of the developed framework is computationally efficient and can automatically detect spatial changes between as-designed model and as-built data or between two sets of as-built data collected using 3D laser scanning technology. Then a spatial change classification algorithm automatically classifies the detected spatial changes as global (rigid body motion) and local deformations (tension, compression). Finally, a change correlation technique utilizes a qualitative shape-based reasoning approach for identifying correlated deformations of structure elements connected at joints that contradicts the joint equilibrium. Those contradicting deformations can help to eliminate improbable loading combinations therefore guiding the loading path analysis of the structure.Dissertation/ThesisDoctoral Dissertation Civil and Environmental Engineering 201
Navigace mobilnĂch robotĆŻ v neznĂĄmĂ©m prostĆedĂ s vyuĆŸitĂm mÄĆenĂ vzdĂĄlenostĂ
The ability of a robot to navigate itself in the environment is a crucial step towards its autonomy. Navigation as a subtask of the development of autonomous robots is the subject of this thesis, focusing on the development of a method for simultaneous localization an mapping (SLAM) of mobile robots in six degrees of freedom (DOF). As a part of this research, a platform for 3D range data acquisition based on a continuously inclined laser rangefinder was developed. This platform is presented, evaluating the measurements and also presenting the robotic equipment on which the platform can be fitted. The localization and mapping task is equal to the registration of multiple 3D images into a common frame of reference. For this purpose, a method based on the Iterative Closest Point (ICP) algorithm was developed. First, the originally implemented SLAM method is presented, focusing on the time-wise performance and the registration quality issues introduced by the implemented algorithms. In order to accelerate and improve the quality of the time-demanding 6DOF image registration, an extended method was developed. The major extension is the introduction of a factorized registration, extracting 2D representations of vertical objects called leveled maps from the 3D point sets, ensuring these representations are 3DOF invariant. The extracted representations are registered in 3DOF using ICP algorithm, allowing pre-alignment of the 3D data for the subsequent robust 6DOF ICP based registration. The extended method is presented, showing all important modifications to the original method. The developed registration method was evaluated using real 3D data acquired in different indoor environments, examining the benefits of the factorization and other extensions as well as the performance of the original ICP based method. The factorization gives promising results compared to a single phase 6DOF registration in vertically structured environments. Also, the disadvantages of the method are discussed, proposing possible solutions. Finally, the future prospects of the research are presented.Schopnost lokalizace a navigace je podmĂnkou autonomnĂho provozu mobilnĂch robotĆŻ. PĆedmÄtem tĂ©to disertaÄnĂ prĂĄce jsou navigaÄnĂ metody se zamÄĆenĂm na metodu pro simultĂĄnnĂ lokalizaci a mapovĂĄnĂ (SLAM) mobilnĂch robotĆŻ v ĆĄesti stupnĂch volnosti (6DOF). NedĂlnou souÄĂĄstĂ tohoto vĂœzkumu byl vĂœvoj platformy pro sbÄr 3D vzdĂĄlenostnĂch dat s vyuĆŸitĂm kontinuĂĄlnÄ naklĂĄpÄnĂ©ho laserovĂ©ho ĆĂĄdkovĂ©ho scanneru. Tato platforma byla vyvinuta jako samostatnĂœ modul, aby mohla bĂœt umĂstÄna na rĆŻznĂ© ĆĄasi mobilnĂch robotĆŻ. Ăkol lokalizace a mapovĂĄnĂ je ekvivalentnĂ registraci vĂce 3D obrazĆŻ do spoleÄnĂ©ho souĆadnĂ©ho systĂ©mu. Pro tyto ĂșÄely byla vyvinuta metoda zaloĆŸenĂĄ na algoritmu Iterative Closest Point Algorithm (ICP). PĆŻvodnÄ implementovanĂĄ verze navigaÄnĂ metody vyuĆŸĂvĂĄ ICP s akceleracĂ pomocĂ kd-stromĆŻ pĆiÄemĆŸ jsou zhodnoceny jejĂ kvalitativnĂ a vĂœkonnostnĂ aspekty. Na zĂĄkladÄ tĂ©to analĂœzy byly vyvinuty rozĆĄĂĆenĂ pĆŻvodnĂ metody zaloĆŸenĂ© na ICP. Jednou z hlavnĂch modifikacĂ je faktorizace registraÄnĂho procesu, kdy tato faktorizace je zaloĆŸena na redukci dat: vytvoĆenĂ 2D âleveledâ map (ve smyslu jednoĂșrovĆovĂœch map) ze 3D vzdĂĄlenostnĂch obrazĆŻ. Pro tuto redukci je technologicky i algoritmicky zajiĆĄtÄna invariantnost tÄchto map vĆŻÄi tĆem stupĆĆŻm volnosti. Tyto redukovanĂ© mapy jsou registrovĂĄny pomocĂ ICP ve zbylĂœch tĆech stupnĂch volnosti, pĆiÄemĆŸ zĂskanĂĄ transformace je aplikovĂĄna na 3D data za ĂșÄelem pĆed-registrace 3D obrazĆŻ. NĂĄslednÄ je provedena robustnĂ 6DOF registrace. RozĆĄĂĆenĂĄ metoda je v disertaÄnĂ prĂĄci v popsĂĄna spolu se vĆĄemi podstatnĂœmi modifikacemi. VyvinutĂĄ metoda byla otestovĂĄna a zhodnocena s vyuĆŸitĂm skuteÄnĂœch 3D vzdĂĄlenostnĂch dat namÄĆenĂœch v rĆŻznĂœch vnitĆnĂch prostĆedĂch. Jsou zhodnoceny pĆĂnosy faktorizace a jinĂœch modifikacĂ ve srovnĂĄnĂ s pĆŻvodnĂ jednofĂĄzovou 6DOF registracĂ, takĂ© jsou zmĂnÄny nevĂœhody implementovanĂ© metody a navrĆŸeny zpĆŻsoby jejich ĆeĆĄenĂ. Nakonec nĂĄsleduje nĂĄvrh budoucĂho vĂœzkumu a diskuse o moĆŸnostech dalĆĄĂho rozvoje.
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Intelligent laser scanning for computer aided manufacture.
Reverse engineering requires the acquisition of large amounts of data describing the surface of an object, sufficient to replicate that object accurately using appropriate fabrication techniques. This is important within a wide range of commercial and scientific fields where CAD models may be unavailable for parts that must be duplicated or modified, or where a physical model is used as a prototype. The three-dimensional digitisation of objects is an essential first step in reverse engineering. Optical triangulation laser sensors are one of the most popular and common non-contact methods used in the data acquisition process today. They provide the means for high resolution scanning of complex objects. Multiple scans of the object are usually required to capture the full 3D profile of the object. A number of factors, including scan resolution, system optics and the precision of the mechanical parts comprising the system may affect the accuracy of the process. A single perspective optical triangulation sensor provides an inexpensive method for the acquisition of 3D range image data
A machine learning approach to pedestrian detection for autonomous vehicles using High-Definition 3D Range Data
This article describes an automated sensor-based system to detect pedestrians in an autonomous vehicle application. Although the vehicle is equipped with a broad set of sensors, the article focuses on the processing of the information generated by a Velodyne HDL-64E LIDAR sensor. The cloud of points generated by the sensor (more than 1 million points per revolution) is processed to detect pedestrians, by selecting cubic shapes and applying machine vision and machine learning algorithms to the XY, XZ, and YZ projections of the points contained in the cube. The work relates an exhaustive analysis of the performance of three different machine learning algorithms: k-Nearest Neighbours (kNN), NaĂŻve Bayes classifier (NBC), and Support Vector Machine (SVM). These algorithms have been trained with 1931 samples. The final performance of the method, measured a real traffic scenery, which contained 16 pedestrians and 469 samples of non-pedestrians, shows sensitivity (81.2%), accuracy (96.2%) and specificity (96.8%).This work was partially supported by ViSelTR (ref. TIN2012-39279) and cDrone (ref. TIN2013-45920-R) projects of the Spanish Government, and the âResearch Programme for Groups of Scientific Excellence at Region of Murciaâ of the Seneca Foundation (Agency for Science and Technology of the Region of Murciaâ19895/GERM/15). 3D LIDAR has been funded by UPCA13-3E-1929 infrastructure projects of the Spanish Government. Diego Alonso wishes to thank the Spanish Ministerio de EducaciĂłn, Cultura y Deporte, Subprograma Estatal de Movilidad, Plan Estatal de InvestigaciĂłn CientĂfica y TĂ©cnica y de InnovaciĂłn 2013â2016 for grant CAS14/00238
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