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This paper describes a 3D modeling method for wide area outdoor environments which is based on integrating omnidirectional range and color images. In the proposed method, outdoor scenes can be efficiently digitized by an omnidirectional laser rangefinder which can obtain a 3D shape with high-accuracy and by an omnidirectional multi-camera system (OMS) which can capture a high-resolution color image. Multiple range images are registered by minimizing the distances between corresponding points in the different range images. In order to register multiple range images stably, points on plane portions detected from the range data are used in registration process. The position and orientation acquired by RTK-GPS and gyroscope are used as initial values of simultaneous registration. The 3D model obtained by registration of range data is mapped by textures selected from omnidirectional images in consideration of the resolution of texture and occlusions of the model. In experiments, we have carried out 3D modeling of our campus with the proposed method., 3DIM 2005 : 5th International Conference on 3-D Digital Imaging and Modeling , Jun 13-16, 2005 , Ottawa, Ontario, Canada

The area of biometric systems has passed through considerable advancement in the past two decades. Supporting of security provision plays a key role in many branches. There are large amount of the biometrical markers which can be utilized in the person identification process. One of the possible ways is a method which uses a hand shape contour classification. The presented paper solves the problem of hand contours classification with use of a Simple Genetic Algorithm (SGA). The foundations of the SGA were established in 1950’s, but an improvement process of the SGA continues. The hand contour for the classification purposes is obtained from a color image from a biometric scanner. The biometric scanner has fixed pegs to hold the hand, or the hand can be freely placed on the scanning area. A core of the proposed estimator is an Iterative Closes Point algorithm which enables matching of the two point-clouds and expressing their dissimilarity regarding the elected metrics. In the experimental section, a large number of experiments were performed with a different setting of the SGA working parameters. Beside the capability to correctly align/match the hand contours, selected standard benchmark tests were performed with a corresponding number of dimensions. The presented estimator solves the thee-dimensional optimization task. Based on experimental results, it was proven that in the case of identical contours the proposed method, which utilizes the SGA optimizer, provides very accurate results., V posledních dvou dekádách prošla vědní oblast biometrické identifikace osob poměrně masivním vývojem. Důvod je ten, že zajišťování bezpečnosti patří mezi klíčové otázky v mnoha odvětvích. Existuje nepřeberné množství tzv. biometrických ukazatelů, které je možné využít pro identifikaci osob. Jednou z cest, jak provádět identifikaci je využít tvar kontury lidské ruky. Předkládaný článek řeší úlohu klasifikace kontur rukou s využitím jednoduchého genetického algoritmu (SGA). Základy SGA byly položeny již před desítkami let v 50. letech minulého století, ale proces vývoje a vylepšování původních návrhů stále pokračuje. Kontura lidské ruky je získána z klasického RGB snímku z digitálního fotoaparátu. Jádro navržené metody využívá algoritmus Iterative Closest Point (ICP), který umožňuje porovnávat dva shluky bodů, které tvoří konturu ruky. Snímek ruky může být pořízen např. z biometrického scanneru s nebo bez stavících kolíků nebo jednoduše z volně položené ruky na vhodném podkladu. Bylo provedeno velké množství experimentů s algoritmem SGA a především s různým nastavením pracovních parametrů. Bylo také provedeno zkoumání chování úlohy pro různý počet dimenzí resp. různý počet pohyblivých prstů kontury ruky. Presentovaný estimátor řeší primárně třídimenzionální optimalizační úlohu – prsty jsou nepohyblivé, ale řeší také vícedimenzionální úlohu, kde se pohybují všechny prsty. Na základě experimentálních výsledků bylo zjištěno, že minimálně pro tří dimenzionální úlohu estimátor poskytuje dobré výsledky.

This thesis addresses the problem of registering one point set with respect to another. This problem arises in the context of the use of CMM/Scanners to inspect objects especially with freeform surfaces. The tolerance verification process now requires the comparison of measured points with the nominal geometry. This entails placement of the measured point set in the same reference frame as the nominal model. This problem is referred to as the registration or localization problem. In the most general form the tolerance verification task involves registering multiple point sets corresponding to multi-step scan of an object with respect to the nominal CAD model. This problem is addressed in three phases. This thesis presents a novel approach to automated inspection by matching point sets based on the Iterative Closest Point (ICP) algorithm. The Modified ICP (MICP) algorithm presented in the thesis improves upon the existing methods through the use of a localized region based triangulation technique to obtain correspondences for all the inspection points and achieves dramatic reduction in computational effort. The use of point sets to represent the nominal surface and shapes enables handling different systems and formats. Next, the thesis addresses the important problem of establishing registration between point sets in different reference frames when the initial relative pose between them is significantly large. A novel initial pose invariant methodology has been developed. Finally, the above approach is extended to registration of multiview inspection data sets based on acquisition of transformation information of each inspection view using the virtual gauging concept. This thesis describes implementation to address each of these problems in the area of automated registration and verification leading towards automatic inspection.

A critical factor of endoprostheses is the quality of the tribological pairing. The objective of this research project is to manufacture stochastically porous aluminum oxide surface coatings with high wear resistance and an active friction minimization. There are many experimental and computational techniques from mercury porosimetry to imaging methods for studying porous materials, however, the characterization of disordered pore networks is still a great challenge. To meet this challenge it is striven to gain a three dimensional high resolution reconstruction of the surface. In this work, the reconstruction is approached by repeatedly milling down the surface by a fixed decrement while measuring each layer using a confocal laser scanning microscope (CLSM). The so acquired depth data of the successive layers is then registered pairwise. Within this work a direct registration approach is deployed and implemented in two steps, a coarse and a fine alignment. The coarse alignment of the depth data is limited to a translational shift which occurs in horizontal direction due to placing the sample in turns under the CLSM and the milling machine and in vertical direction due to the milling process itself. The shift is determined by an approach utilizing 3D phase correlation. The fine alignment is implemented by the Trimmed Iterative Closest Point algorithm, matching the most likely common pixels roughly specified by an estimated overlap rate. With the presented two-step approach a proper 3D registration of the successive depth data of the layer is obtained. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

©2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., Low resolution commercial 3D sensors contribute to computer vision tasks even better when the analysis is carried out in a combination with higher resolution image data. This requires registration of 2D images to unorganized 3D point clouds. In this paper we present a framework for 2D-3D data fusion to obtain directly the camera pose of a 2D color image in relation to a 3D point cloud. It includes a novel multiscale intensity feature detection algorithm and a modified ICP procedure based on point-to-line distances. The framework is generic for several data types (such as CAD designs or LiDAR data without photometric information), and results show that performance is comparable to the state of the art, while avoiding manual markers or specific patterns on the data., Peer Reviewed, Postprint (author's final draft)

Autonomous vehicles rely on GPS aided by motion sensors to localize globally within the road network. However, not all driving surfaces have satellite visibility. Therefore, it is important to augment these systems with localization based on environmental sensing such as cameras, lidar and radar in order to increase reliability and robustness. In this work we look at using radar for localization. Radar sensors are available in compact format devices well suited to automotive applications. Past work on localization using radar in automotive applications has been based on careful sensor modeling and Sequential Monte Carlo, (Particle) filtering. In this work we investigate the use of the Iterative Closest Point, ICP, algorithm together with an Extended Kalman filter, EKF, for localizing a vehicle equipped with automotive grade radars. Experiments using data acquired on public roads shows that this computationally simpler approach yields sufficiently accurate results on par with more complex methods., QC 20160929, iQMatic VINNOVA

Autonomous vehicles rely on GPS aided by motion sensors to localize globally within the road network. However, not all driving surfaces have satellite visibility. Therefore, it is important to augment these systems with localization based on environmental sensing such as cameras, lidar and radar in order to increase reliability and robustness. In this work we look at using radar for localization. Radar sensors are available in compact format devices well suited to automotive applications. Past work on localization using radar in automotive applications has been based on careful sensor modeling and Sequential Monte Carlo, (Particle) filtering. In this work we investigate the use of the Iterative Closest Point, ICP, algorithm together with an Extended Kalman filter, EKF, for localizing a vehicle equipped with automotive grade radars. Experiments using data acquired on public roads shows that this computationally simpler approach yields sufficiently accurate results on par with more complex methods., QC 20160929, iQMatic VINNOVA

The Iterative Closest Point (ICP) algorithm is currently one of the most popular methods for rigid registration so that it has become the standard in the Robotics and Computer Vision communities. Many applications take advantage of it to align 2D/3D surfaces due to its popularity and simplicity. Nevertheless, some of its phases present a high computational cost thus rendering impossible some of its applications. In this work, it is proposed an efficient approach for the matching phase of the Iterative Closest Point algorithm. This stage is the main bottleneck of that method so that any efficiency improvement has a great positive impact on the performance of the algorithm. The proposal consists in using low computational cost point-to-point distance metrics instead of classic Euclidean one. The candidates analysed are the Chebyshev and Manhattan distance metrics due to their simpler formulation. The experiments carried out have validated the performance, robustness and quality of the proposal. Different experimental cases and configurations have been set up including a heterogeneous set of 3D figures, several scenarios with partial data and random noise. The results prove that an average speed up of 14% can be obtained while preserving the convergence properties of the algorithm and the quality of the final results.

The Iterative Closest Point (ICP) algorithm is currently one of the most popular methods for rigid registration so that it has become the standard in the Robotics and Computer Vision communities. Many applications take advantage of it to align 2D/3D surfaces due to its popularity and simplicity. Nevertheless, some of its phases present a high computational cost thus rendering impossible some of its applications. In this work, it is proposed an efficient approach for the matching phase of the Iterative Closest Point algorithm. This stage is the main bottleneck of that method so that any efficiency improvement has a great positive impact on the performance of the algorithm. The proposal consists in using low computational cost point-to-point distance metrics instead of classic Euclidean one. The candidates analysed are the Chebyshev and Manhattan distance metrics due to their simpler formulation. The experiments carried out have validated the performance, robustness and quality of the proposal. Different experimental cases and configurations have been set up including a heterogeneous set of 3D figures, several scenarios with partial data and random noise. The results prove that an average speed up of 14% can be obtained while preserving the convergence properties of the algorithm and the quality of the final results.

Autonomous vehicles rely on GPS aided by motion sensors to localize globally within the road network. However, not all driving surfaces have satellite visibility. Therefore, it is important to augment these systems with localization based on environmental sensing such as cameras, lidar and radar in order to increase reliability and robustness. In this work we look at using radar for localization. Radar sensors are available in compact format devices well suited to automotive applications. Past work on localization using radar in automotive applications has been based on careful sensor modeling and Sequential Monte Carlo, (Particle) filtering. In this work we investigate the use of the Iterative Closest Point, ICP, algorithm together with an Extended Kalman filter, EKF, for localizing a vehicle equipped with automotive grade radars. Experiments using data acquired on public roads shows that this computationally simpler approach yields sufficiently accurate results on par with more complex methods., QC 20160929, iQMatic VINNOVA

Autonomous vehicles rely on GPS aided by motion sensors to localize globally within the road network. However, not all driving surfaces have satellite visibility. Therefore, it is important to augment these systems with localization based on environmental sensing such as cameras, lidar and radar in order to increase reliability and robustness. In this work we look at using radar for localization. Radar sensors are available in compact format devices well suited to automotive applications. Past work on localization using radar in automotive applications has been based on careful sensor modeling and Sequential Monte Carlo, (Particle) filtering. In this work we investigate the use of the Iterative Closest Point, ICP, algorithm together with an Extended Kalman filter, EKF, for localizing a vehicle equipped with automotive grade radars. Experiments using data acquired on public roads shows that this computationally simpler approach yields sufficiently accurate results on par with more complex methods., QC 20160929, iQMatic VINNOVA

Autonomous vehicles rely on GPS aided by motion sensors to localize globally within the road network. However, not all driving surfaces have satellite visibility. Therefore, it is important to augment these systems with localization based on environmental sensing such as cameras, lidar and radar in order to increase reliability and robustness. In this work we look at using radar for localization. Radar sensors are available in compact format devices well suited to automotive applications. Past work on localization using radar in automotive applications has been based on careful sensor modeling and Sequential Monte Carlo, (Particle) filtering. In this work we investigate the use of the Iterative Closest Point, ICP, algorithm together with an Extended Kalman filter, EKF, for localizing a vehicle equipped with automotive grade radars. Experiments using data acquired on public roads shows that this computationally simpler approach yields sufficiently accurate results on par with more complex methods., QC 20160929, iQMatic VINNOVA

Given that 3D scan matching is such a central part of the perception pipeline for robots, thorough and large-scale investigations of scan matching performance are still surprisingly few. A crucial part of the scientific method is to perform experiments that can be replicated by other researchers in order to compare different results. In light of this fact, this paper presents a thorough comparison of 3D scan registration algorithms using a recently published benchmark protocol which makes use of a publicly available challenging data set that covers a wide range of environments. In particular, we evaluate two types of recent 3D registration algorithms - one local and one global. Both approaches take local surface structure into account, rather than matching individual points. After well over 100 000 individual tests, we conclude that algorithms using the normal distributions transform (NDT) provides accurate results compared to a modern implementation of the iterative closest point (ICP) method, when faced with scan data that has little overlap and weak geometric structure. We also demonstrate that the minimally uncertain maximum consensus (MUMC) algorithm provides accurate results in structured environments without needing an initial guess, and that it provides useful measures to detect whether it has succeeded or not. We also propose two amendments to the experimental protocol, in order to provide more valuable results in future implementations., ALLO, SPENCER, RobLog

Given that 3D scan matching is such a central part of the perception pipeline for robots, thorough and large-scale investigations of scan matching performance are still surprisingly few. A crucial part of the scientific method is to perform experiments that can be replicated by other researchers in order to compare different results. In light of this fact, this paper presents a thorough comparison of 3D scan registration algorithms using a recently published benchmark protocol which makes use of a publicly available challenging data set that covers a wide range of environments. In particular, we evaluate two types of recent 3D registration algorithms - one local and one global. Both approaches take local surface structure into account, rather than matching individual points. After well over 100 000 individual tests, we conclude that algorithms using the normal distributions transform (NDT) provides accurate results compared to a modern implementation of the iterative closest point (ICP) method, when faced with scan data that has little overlap and weak geometric structure. We also demonstrate that the minimally uncertain maximum consensus (MUMC) algorithm provides accurate results in structured environments without needing an initial guess, and that it provides useful measures to detect whether it has succeeded or not. We also propose two amendments to the experimental protocol, in order to provide more valuable results in future implementations., ALLO, SPENCER, RobLog

Given that 3D scan matching is such a central part of the perception pipeline for robots, thorough and large-scale investigations of scan matching performance are still surprisingly few. A crucial part of the scientific method is to perform experiments that can be replicated by other researchers in order to compare different results. In light of this fact, this paper presents a thorough comparison of 3D scan registration algorithms using a recently published benchmark protocol which makes use of a publicly available challenging data set that covers a wide range of environments. In particular, we evaluate two types of recent 3D registration algorithms - one local and one global. Both approaches take local surface structure into account, rather than matching individual points. After well over 100 000 individual tests, we conclude that algorithms using the normal distributions transform (NDT) provides accurate results compared to a modern implementation of the iterative closest point (ICP) method, when faced with scan data that has little overlap and weak geometric structure. We also demonstrate that the minimally uncertain maximum consensus (MUMC) algorithm provides accurate results in structured environments without needing an initial guess, and that it provides useful measures to detect whether it has succeeded or not. We also propose two amendments to the experimental protocol, in order to provide more valuable results in future implementations., ALLO, SPENCER, RobLog

Given that 3D scan matching is such a central part of the perception pipeline for robots, thorough and large-scale investigations of scan matching performance are still surprisingly few. A crucial part of the scientific method is to perform experiments that can be replicated by other researchers in order to compare different results. In light of this fact, this paper presents a thorough comparison of 3D scan registration algorithms using a recently published benchmark protocol which makes use of a publicly available challenging data set that covers a wide range of environments. In particular, we evaluate two types of recent 3D registration algorithms - one local and one global. Both approaches take local surface structure into account, rather than matching individual points. After well over 100 000 individual tests, we conclude that algorithms using the normal distributions transform (NDT) provides accurate results compared to a modern implementation of the iterative closest point (ICP) method, when faced with scan data that has little overlap and weak geometric structure. We also demonstrate that the minimally uncertain maximum consensus (MUMC) algorithm provides accurate results in structured environments without needing an initial guess, and that it provides useful measures to detect whether it has succeeded or not. We also propose two amendments to the experimental protocol, in order to provide more valuable results in future implementations., ALLO, SPENCER, RobLog

Given that 3D scan matching is such a central part of the perception pipeline for robots, thorough and large-scale investigations of scan matching performance are still surprisingly few. A crucial part of the scientific method is to perform experiments that can be replicated by other researchers in order to compare different results. In light of this fact, this paper presents a thorough comparison of 3D scan registration algorithms using a recently published benchmark protocol which makes use of a publicly available challenging data set that covers a wide range of environments. In particular, we evaluate two types of recent 3D registration algorithms - one local and one global. Both approaches take local surface structure into account, rather than matching individual points. After well over 100 000 individual tests, we conclude that algorithms using the normal distributions transform (NDT) provides accurate results compared to a modern implementation of the iterative closest point (ICP) method, when faced with scan data that has little overlap and weak geometric structure. We also demonstrate that the minimally uncertain maximum consensus (MUMC) algorithm provides accurate results in structured environments without needing an initial guess, and that it provides useful measures to detect whether it has succeeded or not. We also propose two amendments to the experimental protocol, in order to provide more valuable results in future implementations., ALLO, SPENCER, RobLog

A newly developed middle-frequency (2 MHz) inductively coupled plasma (ICP) source with internal oscillating current is used to treat biodegradable food packaging surfaces. Initially hydrophilic packaging turns to hydrophobic after being processed by ICP plasma. The investigation of optical emission from hydrocarbon radicals in the Ar/ CH4 plasma helps us to understand the property of the hydrophobicity of the surfaces. © 2008 IEEE.

application/pdf, This paper describes a 3D modeling method for wide area outdoor environments which is based on integrating omnidirectional range and color images. In the proposed method, outdoor scenes can be efficiently digitized by an omnidirectional laser rangefinder which can obtain a 3D shape with high-accuracy and by an omnidirectional multi-camera system (OMS) which can capture a high-resolution color image. Multiple range images are registered by minimizing the distances between corresponding points in the different range images. In order to register multiple range images stably, points on plane portions detected from the range data are used in registration process. The position and orientation acquired by RTK-GPS and gyroscope are used as initial values of simultaneous registration. The 3D model obtained by registration of range data is mapped by textures selected from omnidirectional images in consideration of the resolution of texture and occlusions of the model. In experiments, we have carried out 3D modeling of our campus with the proposed method., 3DIM 2005 : 5th International Conference on 3-D Digital Imaging and Modeling , Jun 13-16, 2005 , Ottawa, Ontario, Canada, conference paper

This paper describes a 3D modeling method for wide area outdoor environments which is based on integrating omnidirectional range and color images. In the proposed method, outdoor scenes can be efficiently digitized by an omnidirectional laser rangefinder which can obtain a 3D shape with high-accuracy and by an omnidirectional multi-camera system (OMS) which can capture a high-resolution color image. Multiple range images are registered by minimizing the distances between corresponding points in the different range images. In order to register multiple range images stably, points on plane portions detected from the range data are used in registration process. The position and orientation acquired by RTK-GPS and gyroscope are used as initial values of simultaneous registration. The 3D model obtained by registration of range data is mapped by textures selected from omnidirectional images in consideration of the resolution of texture and occlusions of the model. In experiments, we have carried out 3D modeling of our campus with the proposed method.

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 ar, Schopnost lokalizace a navigace je podmínkou autonomního provozu mobilních robotů. Předmětem této disertační práce jsou navigační metody se zaměřením na metodu pro simultánní lokalizaci a mapování (SLAM) mobilních robotů v šesti stupních volnosti (6DOF). Nedílnou součástí tohoto výzkumu byl vývoj platformy pro sběr 3D vzdálenostních dat s využitím kontinuálně naklápěného laserového řádkového scanneru. Tato platforma byla vyvinuta jako samostatný modul, aby mohla být umístěna na různé šasi mobilních robotů. Úkol lokalizace a mapování je ekvivalentní registraci více 3D obrazů do společného souřadného systému. Pro tyto účely byla vyvinuta metoda založená na algoritmu Iterative Closest Point Algorithm (ICP). Původně implementovaná verze navigační metody využívá ICP s akcelerací pomocí kd-stromů přičemž jsou zhodnoceny její kvalitativní a výkonnostní aspekty. Na základě této analýzy byly vyvinuty rozšíření původní metody založené na ICP. Jednou z hlavních modifikací je faktorizace registračního procesu, kdy tato faktorizace je založena na redukci dat: vytvoření 2D „leveled“ map (ve smyslu jednoúrovňových map) ze 3D vzdálenostních obrazů. Pro tuto redukci je technologicky i algoritmicky zajištěna invariantnost těchto map vůči třem stupňům volnosti. Tyto redukované mapy jsou registrovány pomocí ICP ve zbylých třech stupních volnosti, přičemž získaná transformace je aplikována na 3D data za účelem před-registrace 3D obrazů. Následně je provedena robustní 6DOF registrace. Rozšířená metoda je v disertační práci v popsána spolu se všemi podstatnými modifikacemi. Vyvinutá metoda byla otestována a zhodnocena s využitím skutečných 3D vzdálenostních dat naměřených v různých vnitřních prostředích. Jsou zhodnoceny přínosy faktorizace a jiných modifikací ve srovnání s původní jednofázovou 6DOF registrací, také jsou zmíněny nevýhody implementované metody a navrženy způsoby jejich řešení. Nakonec následuje návrh budoucího výzkumu a diskuse o možnostech dalšího rozvoje.

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 ar, Schopnost lokalizace a navigace je podmínkou autonomního provozu mobilních robotů. Předmětem této disertační práce jsou navigační metody se zaměřením na metodu pro simultánní lokalizaci a mapování (SLAM) mobilních robotů v šesti stupních volnosti (6DOF). Nedílnou součástí tohoto výzkumu byl vývoj platformy pro sběr 3D vzdálenostních dat s využitím kontinuálně naklápěného laserového řádkového scanneru. Tato platforma byla vyvinuta jako samostatný modul, aby mohla být umístěna na různé šasi mobilních robotů. Úkol lokalizace a mapování je ekvivalentní registraci více 3D obrazů do společného souřadného systému. Pro tyto účely byla vyvinuta metoda založená na algoritmu Iterative Closest Point Algorithm (ICP). Původně implementovaná verze navigační metody využívá ICP s akcelerací pomocí kd-stromů přičemž jsou zhodnoceny její kvalitativní a výkonnostní aspekty. Na základě této analýzy byly vyvinuty rozšíření původní metody založené na ICP. Jednou z hlavních modifikací je faktorizace registračního procesu, kdy tato faktorizace je založena na redukci dat: vytvoření 2D „leveled“ map (ve smyslu jednoúrovňových map) ze 3D vzdálenostních obrazů. Pro tuto redukci je technologicky i algoritmicky zajištěna invariantnost těchto map vůči třem stupňům volnosti. Tyto redukované mapy jsou registrovány pomocí ICP ve zbylých třech stupních volnosti, přičemž získaná transformace je aplikována na 3D data za účelem před-registrace 3D obrazů. Následně je provedena robustní 6DOF registrace. Rozšířená metoda je v disertační práci v popsána spolu se všemi podstatnými modifikacemi. Vyvinutá metoda byla otestována a zhodnocena s využitím skutečných 3D vzdálenostních dat naměřených v různých vnitřních prostředích. Jsou zhodnoceny přínosy faktorizace a jiných modifikací ve srovnání s původní jednofázovou 6DOF registrací, také jsou zmíněny nevýhody implementované metody a navrženy způsoby jejich řešení. Nakonec následuje návrh budoucího výzkumu a diskuse o možnostech dalšího rozvoje.

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 ar, Schopnost lokalizace a navigace je podmínkou autonomního provozu mobilních robotů. Předmětem této disertační práce jsou navigační metody se zaměřením na metodu pro simultánní lokalizaci a mapování (SLAM) mobilních robotů v šesti stupních volnosti (6DOF). Nedílnou součástí tohoto výzkumu byl vývoj platformy pro sběr 3D vzdálenostních dat s využitím kontinuálně naklápěného laserového řádkového scanneru. Tato platforma byla vyvinuta jako samostatný modul, aby mohla být umístěna na různé šasi mobilních robotů. Úkol lokalizace a mapování je ekvivalentní registraci více 3D obrazů do společného souřadného systému. Pro tyto účely byla vyvinuta metoda založená na algoritmu Iterative Closest Point Algorithm (ICP). Původně implementovaná verze navigační metody využívá ICP s akcelerací pomocí kd-stromů přičemž jsou zhodnoceny její kvalitativní a výkonnostní aspekty. Na základě této analýzy byly vyvinuty rozšíření původní metody založené na ICP. Jednou z hlavních modifikací je faktorizace registračního procesu, kdy tato faktorizace je založena na redukci dat: vytvoření 2D „leveled“ map (ve smyslu jednoúrovňových map) ze 3D vzdálenostních obrazů. Pro tuto redukci je technologicky i algoritmicky zajištěna invariantnost těchto map vůči třem stupňům volnosti. Tyto redukované mapy jsou registrovány pomocí ICP ve zbylých třech stupních volnosti, přičemž získaná transformace je aplikována na 3D data za účelem před-registrace 3D obrazů. Následně je provedena robustní 6DOF registrace. Rozšířená metoda je v disertační práci v popsána spolu se všemi podstatnými modifikacemi. Vyvinutá metoda byla otestována a zhodnocena s využitím skutečných 3D vzdálenostních dat naměřených v různých vnitřních prostředích. Jsou zhodnoceny přínosy faktorizace a jiných modifikací ve srovnání s původní jednofázovou 6DOF registrací, také jsou zmíněny nevýhody implementované metody a navrženy způsoby jejich řešení. Nakonec následuje návrh budoucího výzkumu a diskuse o možnostech dalšího rozvoje.

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 ar, Schopnost lokalizace a navigace je podmínkou autonomního provozu mobilních robotů. Předmětem této disertační práce jsou navigační metody se zaměřením na metodu pro simultánní lokalizaci a mapování (SLAM) mobilních robotů v šesti stupních volnosti (6DOF). Nedílnou součástí tohoto výzkumu byl vývoj platformy pro sběr 3D vzdálenostních dat s využitím kontinuálně naklápěného laserového řádkového scanneru. Tato platforma byla vyvinuta jako samostatný modul, aby mohla být umístěna na různé šasi mobilních robotů. Úkol lokalizace a mapování je ekvivalentní registraci více 3D obrazů do společného souřadného systému. Pro tyto účely byla vyvinuta metoda založená na algoritmu Iterative Closest Point Algorithm (ICP). Původně implementovaná verze navigační metody využívá ICP s akcelerací pomocí kd-stromů přičemž jsou zhodnoceny její kvalitativní a výkonnostní aspekty. Na základě této analýzy byly vyvinuty rozšíření původní metody založené na ICP. Jednou z hlavních modifikací je faktorizace registračního procesu, kdy tato faktorizace je založena na redukci dat: vytvoření 2D „leveled“ map (ve smyslu jednoúrovňových map) ze 3D vzdálenostních obrazů. Pro tuto redukci je technologicky i algoritmicky zajištěna invariantnost těchto map vůči třem stupňům volnosti. Tyto redukované mapy jsou registrovány pomocí ICP ve zbylých třech stupních volnosti, přičemž získaná transformace je aplikována na 3D data za účelem před-registrace 3D obrazů. Následně je provedena robustní 6DOF registrace. Rozšířená metoda je v disertační práci v popsána spolu se všemi podstatnými modifikacemi. Vyvinutá metoda byla otestována a zhodnocena s využitím skutečných 3D vzdálenostních dat naměřených v různých vnitřních prostředích. Jsou zhodnoceny přínosy faktorizace a jiných modifikací ve srovnání s původní jednofázovou 6DOF registrací, také jsou zmíněny nevýhody implementované metody a navrženy způsoby jejich řešení. Nakonec následuje návrh budoucího výzkumu a diskuse o možnostech dalšího rozvoje.

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 ar, Schopnost lokalizace a navigace je podmínkou autonomního provozu mobilních robotů. Předmětem této disertační práce jsou navigační metody se zaměřením na metodu pro simultánní lokalizaci a mapování (SLAM) mobilních robotů v šesti stupních volnosti (6DOF). Nedílnou součástí tohoto výzkumu byl vývoj platformy pro sběr 3D vzdálenostních dat s využitím kontinuálně naklápěného laserového řádkového scanneru. Tato platforma byla vyvinuta jako samostatný modul, aby mohla být umístěna na různé šasi mobilních robotů. Úkol lokalizace a mapování je ekvivalentní registraci více 3D obrazů do společného souřadného systému. Pro tyto účely byla vyvinuta metoda založená na algoritmu Iterative Closest Point Algorithm (ICP). Původně implementovaná verze navigační metody využívá ICP s akcelerací pomocí kd-stromů přičemž jsou zhodnoceny její kvalitativní a výkonnostní aspekty. Na základě této analýzy byly vyvinuty rozšíření původní metody založené na ICP. Jednou z hlavních modifikací je faktorizace registračního procesu, kdy tato faktorizace je založena na redukci dat: vytvoření 2D „leveled“ map (ve smyslu jednoúrovňových map) ze 3D vzdálenostních obrazů. Pro tuto redukci je technologicky i algoritmicky zajištěna invariantnost těchto map vůči třem stupňům volnosti. Tyto redukované mapy jsou registrovány pomocí ICP ve zbylých třech stupních volnosti, přičemž získaná transformace je aplikována na 3D data za účelem před-registrace 3D obrazů. Následně je provedena robustní 6DOF registrace. Rozšířená metoda je v disertační práci v popsána spolu se všemi podstatnými modifikacemi. Vyvinutá metoda byla otestována a zhodnocena s využitím skutečných 3D vzdálenostních dat naměřených v různých vnitřních prostředích. Jsou zhodnoceny přínosy faktorizace a jiných modifikací ve srovnání s původní jednofázovou 6DOF registrací, také jsou zmíněny nevýhody implementované metody a navrženy způsoby jejich řešení. Nakonec následuje návrh budoucího výzkumu a diskuse o možnostech dalšího rozvoje.

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 ar, Schopnost lokalizace a navigace je podmínkou autonomního provozu mobilních robotů. Předmětem této disertační práce jsou navigační metody se zaměřením na metodu pro simultánní lokalizaci a mapování (SLAM) mobilních robotů v šesti stupních volnosti (6DOF). Nedílnou součástí tohoto výzkumu byl vývoj platformy pro sběr 3D vzdálenostních dat s využitím kontinuálně naklápěného laserového řádkového scanneru. Tato platforma byla vyvinuta jako samostatný modul, aby mohla být umístěna na různé šasi mobilních robotů. Úkol lokalizace a mapování je ekvivalentní registraci více 3D obrazů do společného souřadného systému. Pro tyto účely byla vyvinuta metoda založená na algoritmu Iterative Closest Point Algorithm (ICP). Původně implementovaná verze navigační metody využívá ICP s akcelerací pomocí kd-stromů přičemž jsou zhodnoceny její kvalitativní a výkonnostní aspekty. Na základě této analýzy byly vyvinuty rozšíření původní metody založené na ICP. Jednou z hlavních modifikací je faktorizace registračního procesu, kdy tato faktorizace je založena na redukci dat: vytvoření 2D „leveled“ map (ve smyslu jednoúrovňových map) ze 3D vzdálenostních obrazů. Pro tuto redukci je technologicky i algoritmicky zajištěna invariantnost těchto map vůči třem stupňům volnosti. Tyto redukované mapy jsou registrovány pomocí ICP ve zbylých třech stupních volnosti, přičemž získaná transformace je aplikována na 3D data za účelem před-registrace 3D obrazů. Následně je provedena robustní 6DOF registrace. Rozšířená metoda je v disertační práci v popsána spolu se všemi podstatnými modifikacemi. Vyvinutá metoda byla otestována a zhodnocena s využitím skutečných 3D vzdálenostních dat naměřených v různých vnitřních prostředích. Jsou zhodnoceny přínosy faktorizace a jiných modifikací ve srovnání s původní jednofázovou 6DOF registrací, také jsou zmíněny nevýhody implementované metody a navrženy způsoby jejich řešení. Nakonec následuje návrh budoucího výzkumu a diskuse o možnostech dalšího rozvoje.