Simultaneous localization and mapping is the technique to construct a 3D map of unknown environment. With the increasing popularity of RGB-depth (RGB-D) sensors such as the Microsoft Kinect, there have been much research on capturing and reconstructing 3D environments using a movable RGB-D sensor. The key process behind these kinds of simultaneous location and mapping (SLAM) systems is the iterative closest point or ICP algorithm, which is an iterative algorithm that can estimate the rigid movement of the camera based on the captured 3D point clouds. While ICP is a well-studied algorithm, it is problematic when it is used in scanning large planar regions such as wall surfaces in a room. The lack of depth variations on planar surfaces makes the global alignment an ill-conditioned problem. In this thesis, we present a novel approach for registering 3D point clouds by combining both color and depth information. Instead of directly searching for point correspondences among 3D data, the proposed method first extracts features from the RGB images, and then back-projects the features to the 3D space to identify more reliable correspondences. These color correspondences form the initial input to the ICP procedure which then proceeds to refine the alignment. Experimental results show that our proposed approach can achieve better accuracy than existing SLAMs in reconstructing indoor environments with large planar surfaces.

Šiame darbe tiriami algoritminiai sprendimai mobiliam robotui, leidžiantys aptikti ieškomą objektą bei įvertinti jo poziciją ir orientaciją erdvėje. Atlikus šios srities technologijų analizę surasta įvairių realizacijai tinkamų metodų, tačiau bendro jų efektyvumo palyginimo trūko. Siekiant užpildyti šią spragą realizuota programinė ir techninė įranga, kuria atliktas labiausiai roboto sistemoms tinkamų metodų vertinimas. Algoritmų analizė susideda iš algoritmų tikslumo ir jų veikimo spartos vertinimo panaudojant tam paprastus bei efektyvius metodus. Darbe analizuojamas objektų orientacijos nustatymas iš Kinect kameros gylio duomenų pasitelkiant ICP algoritmą. Atliktas dviejų gylio sistemų spartos ir tikslumo tyrimas parodė, jog Kinect kamera spartos atžvilgiu yra efektyvesnis bei 2-5 kartus tikslesnis sprendimas nei įprastinė stereo kamerų sistema. Objektų aptikimo algoritmų efektyvumo eksperimentuose nustatytas maksimalus aptikimo tikslumas apie 90% bei pasiekta maksimali 15 kadrų/s veikimo sparta analizuojant standartinius VGA 640x480 raiškos vaizdus. Atliktas objektų pozicijos ir orientacijos nustatymo ICP metodo efektyvumo tyrimas parodė, jog vidutinė absoliutinė pozicijos ir orientacijos nustatymo paklaida yra atitinkamai apie 3.4cm bei apie 30 laipsnių, o veikimo sparta apie 2 kadrai/s. Tolesnis optimizavimas arba duomenų kiekio minimizavimas yra būtinas norint pasiekti geresnius veikimo rezultatus mobilioje ribotų resursų roboto sistemoje. Darbe taip pat buvo sėkmingai... [toliau žr. visą tekstą] / This work presents a performance analysis of the state-of-the-art computer vision algorithms for object detection and pose estimation. Initial field study showed that many algorithms for the given problem exist but still their combined comparison was lacking. In order to fill in the existing gap a software and hardware solution was created and the comparison of the most suitable methods for a robot system were done. The analysis consists of detector accuracy and runtime performance evaluation using simple and robust techniques. Object pose estimation via ICP algorithm and stereo vision Kinect depth sensor method was used in this work. A conducted two different stereo system analysis showed that Kinect achieves best runtime performance and its accuracy is 2-5 times more superior than a regular stereo setup. Object detection experiments showcased a maximum object detection accuracy of nearly 90% and speed of 15 fps for standard size VGA 640x480 resolution images. Accomplished object position and orientation estimation experiment using ICP method showed, that average absolute position and orientation detection error is respectively 3.4cm and 30 degrees while the runtime speed – 2 fps. Further optimization and data size minimization is necessary to achieve better efficiency on a resource limited mobile robot platform. The robot hardware system was also successfully implemented and tested in this work for object position and orientation detection.

Point cloud registration is an extensively studied field in computer vision, featuring a variety of existing methods, all aimed at achieving the common objective of determining a transformation that aligns two point clouds. Methods like the Iterative Closet Point (ICP) and Fast Global Registration (FGR) have shown to work well for many years, but recent work has explored different learning-based approaches, showing promising results. This work compares the performance of two learning-based methods GeoTransformer and RegFormer against three baseline methods ICP point-to-point, ICP point-to-plane, and FGR. The comparison was conducted on data provided by the Swedish Defence Research Agency (FOI), where the data was captured with a photon-counting LiDAR sensor. Findings suggest that while ICP point-to-point and ICP point-to-plane exhibit solid performance, the GeoTransformer demonstrates the potential for superior outcomes. Additionally, the RegFormer and FGR perform worse than the ICP variants and the GeoTransformer.

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.

Maintaining an inventory of transportation infrastructure assets is vital for effective management and maintenance. LiDAR (Light Detection and Ranging) can be a useful resource for this purpose by collecting detailed 3D information. Mobile Mapping Systems (MMS) refers to collecting geospatial data by mounting laser scanners on top of a moving vehicle, e.g. a car. The LiDAR collects XYZ-coordinates of the environment by emitting laser pulses toward the surveyed objects. This enables an effective way to store and survey built-up urban areas that otherwise would need an on-site presence. WSP uses Mobile Mapping (MM) to capture and visualize infrastructure, primarily for inventory purposes. Currently, the point cloud registration in the MM-process is labor-intensive, so the company is looking to automate it. This thesis aims to investigate methods to automate the process of point cloud registration that eliminates manual labor. The proposed method was evaluated with regards to its accuracy, advantages and disadvantages. The study area of the thesis was a light rail facility with surrounding residential buildings and vegetation. The proposed method was implemented in Python and utilizes open source libraries. The registration uses Fast Global Registration (FGR) for coarse alignment with Iterative Closest Point (ICP) for fine refinement. The FGR algorithm finds a rigid transformation between a pair of point clouds by establishing a feature correspondence set between the point clouds. The algorithm utilizes Fast Point Feature Histograms (FPFH) that simplifies the description of 3D point relationships as the feature descriptors. The object used for registration is the general area around catenary poles. The segments between poles is adjusted by linear interpolation of the obtained transformation matrices from the registration. The results of this thesis show that automatic point cloud registration is feasible. However, while the proposed method improves registration over raw data, it does not fully replace WSP's current procedure.  The advantages of the proposed method are that it does not require classified data and is open source. The main source of error in the method is the presence of vegetation, and an experiment was conducted to support this hypothesis. The experiment shows that dense vegetation skews the registration, and generates an incorrect transformation matrix. Furthermore, the proposed method is only semi-automated, as it still needs manual post-processing. Accuracy assessment showed that removing outlier, presumably caused by vegetation, improved the planar offsets. Further studies to improve the result could utilize machine learning which could identify and extract poles for registration or remove surrounding vegetation. / Att upprätthålla inventering av tillgångar av transportinfrastruktur är avgörande för effektiv förvaltning och underhåll samt för att tillhandahålla korrekta data och underlätta beslutsfattande. LiDAR-data (Light Detection and Ranging) kan vara ett användbart verktyg för detta ändamål genom att samla in detaljerad 3D-information. Mobile Mapping Systems (MMS) refererar till att samla geospatial data genom att montera laserskannrar ovanpå taket på ett rörligt fordon, exempelvis en bil. LiDAR samlar XYZ-koordinater av kringliggande miljö genom att sända ut laserpulser mot de undersökta objekten. Detta möjliggör ett effektivt sätt att förvara och undersöka bebyggda stadsmiljöer som annars skulle behöva fysisk närvaro. WSP använder Mobile Mapping (MM) för att samla och visualisera infrastruktur, främst för inventeringsändamål. För närvarande är punktmolnregistreringen i MM-processen manuellt arbetskrävande, och därför vill WSP se en automatisering av processen. Detta examensarbete syftar till att undersöka metoder för att automatisera processen för registrering av punktmoln som eliminerar manuellt arbete. Den utvecklade metoden kommer att utvärderas med avseende på dess noggrannhet, för- och nackdelar. Arbetets studieområde är en järnvägsanläggninng med omgivande av bostadshus och vegetation. Den föreslagna metoden implementerades i Python och använder sig av open source-bibliotek. Registeringen tillämpar Fast Global Registration (FGR) för grov justering av punktmolnen, och Iterative Closest Point (ICP) för finjustering. FGR-algoritmen hittar en stel transformation mellan två punktmoln genom att etablera ett set av korresponderande attribut. Algoritmen använder Fast Point Feature Histograms (FPFH) som förenklar euklidiska förhållanden till attributbaserade förhållanden. Objekt som används för registrering är det generella området kring kontaktledningsstolpar. Segmenten mellan stolpar justeras genom linjär interpolation av de erhållna transformationsmatriserna från registreringen. Resultaten av detta arbete visar att automatisk registrering av punktmoln är genomförbar, och att metoden förbättrar registreringen jämfört med den råa datan. Den är dock inte tillräckligt bra för att helt ersätta den nuvarande proceduren som används av WSP. Fördelarna med den föreslagna metoden är att den inte kräver klassificerad data och är open source. Den huvudsakliga felkällan i metoden är förekomsten av vegetation, och ett experiment utfördes för att stödja denna hypotes. Experimentet visar att tät vegetation snedvrider registreringen och genererar en felaktig transformationsmatris. Vidare, är den föreslagna metoden endast semi-automatiserad, eftersom den fortfarande kräver manuell efterbearbetning. Noggrannhetsbedömningn visade att borttagningen av avvikande värden, förmodligen orsakade av vegetation, förbättrade den plana förskjutningen. Vidare studier för att ge ett mer tillfredsställande resultatet kan möjligen vara att använda maskininlärning för att identifiera och extrahera stolpar för matching, samtidigt som växtligheten kan elimineras.

Modern 3D printing technology is becoming a more viable option for use in industrial manufacturing. As the speed and precision of rapid prototyping technology improves, so too must the 3D scanning and verification technology. Current 3D scanning technology (such as CT Scanners) produce the resolution needed for micron precision inspection. However, the method lacks in speed. Some scans can be multiple gigabytes in size taking several minutes to acquire and process. Especially in high volume manufacturing of 3D printed parts, such delays prohibit the widespread adaptation of 3D scanning technology for quality control. The limiting factors of current technology boil down to computational and processing power along with available sensor resolution and operational frequency. Realizing a 3D scanning system that produces micron precision results within a single minute promises to revolutionize the quality control industry. The specific 3D scanning method considered in this thesis utilizes a line profile triangulation sensor with high operational frequency, and a high-precision mechanical actuation apparatus for controlling the scan. By syncing the operational frequency of the sensor to the actuation velocity of the apparatus, a 3D point cloud is rapidly acquired. Processing of the data is then performed using MATLAB on contemporary computing hardware, which includes proper point cloud formatting and implementation of the Iterative Closest Point (ICP) algorithm for point cloud stitching. Theoretical and physical experiments are performed to demonstrate the validity of the method. The prototyped system is shown to produce multiple loosely-registered micron precision point clouds of a 3D printed object that are then stitched together to form a full point cloud representative of the original part. This prototype produces micron precision results in approximately 130 seconds, but the experiments illuminate upon the additional investments by which this time could be further reduced to approach the revolutionizing one-minute milestone.

In wireless networks, reliable communication is a challenging issue due to many attenuation factors such as receiver noise, channel fading, interference and asynchronous delays. Lattice coding and decoding provide efficient solutions to many problems in wireless communications and multiuser information theory. The capability in achieving the fundamental limits, together with simple and efficient transmitter and receiver structures, make the lattice strategy a promising approach. This work deals with problems of lattice detection over fading channels and time asynchronism over the lattice-based compute-and-forward protocol. In multiple-input multiple-output (MIMO) systems, the use of lattice reduction significantly improves the performance of approximate detection techniques. In the first part of this thesis, by taking advantage of the temporal correlation of a Rayleigh fading channel, low complexity lattice reduction methods are investigated. We show that updating the reduced lattice basis adaptively with a careful use of previous channel realizations yields a significant saving in complexity with a minimal degradation in performance. Considering high data rate MIMO systems, we then investigate soft-output detection methods. Using the list sphere decoder (LSD) algorithm, an adaptive method is proposed to reduce the complexity of generating the list for evaluating the log-likelihood ratio (LLR) values. In the second part, by applying the lattice coding and decoding schemes over asynchronous networks, we study the impact of asynchronism on the compute-and-forward strategy. While the key idea in compute-and-forward is to decode a linear synchronous combination of transmitted codewords, the distributed relays receive random asynchronous versions of the combinations. Assuming different asynchronous models, we design the receiver structure prior to the decoder of compute-and-forward so that the achievable rates are maximized at any signal-to-noise-ratio (SNR). Finally, we consider symbol-asynchronous X networks with single antenna nodes over time-invariant channels. We exploit the asynchronism among the received signals in order to design the interference alignment scheme. It is shown that the asynchronism provides correlated channel variations which are proved to be sufficient to implement the vector interference alignment over the constant X network.

In wireless networks, reliable communication is a challenging issue due to many attenuation factors such as receiver noise, channel fading, interference and asynchronous delays. Lattice coding and decoding provide efficient solutions to many problems in wireless communications and multiuser information theory. The capability in achieving the fundamental limits, together with simple and efficient transmitter and receiver structures, make the lattice strategy a promising approach. This work deals with problems of lattice detection over fading channels and time asynchronism over the lattice-based compute-and-forward protocol. In multiple-input multiple-output (MIMO) systems, the use of lattice reduction significantly improves the performance of approximate detection techniques. In the first part of this thesis, by taking advantage of the temporal correlation of a Rayleigh fading channel, low complexity lattice reduction methods are investigated. We show that updating the reduced lattice basis adaptively with a careful use of previous channel realizations yields a significant saving in complexity with a minimal degradation in performance. Considering high data rate MIMO systems, we then investigate soft-output detection methods. Using the list sphere decoder (LSD) algorithm, an adaptive method is proposed to reduce the complexity of generating the list for evaluating the log-likelihood ratio (LLR) values. In the second part, by applying the lattice coding and decoding schemes over asynchronous networks, we study the impact of asynchronism on the compute-and-forward strategy. While the key idea in compute-and-forward is to decode a linear synchronous combination of transmitted codewords, the distributed relays receive random asynchronous versions of the combinations. Assuming different asynchronous models, we design the receiver structure prior to the decoder of compute-and-forward so that the achievable rates are maximized at any signal-to-noise-ratio (SNR). Finally, we consider symbol-asynchronous X networks with single antenna nodes over time-invariant channels. We exploit the asynchronism among the received signals in order to design the interference alignment scheme. It is shown that the asynchronism provides correlated channel variations which are proved to be sufficient to implement the vector interference alignment over the constant X network.

A key component for automated driving is 360◦ environment detection. The recognition capabilities of mod- ern sensors are always limited to their direct field of view. In urban areas a lot of objects occlude important areas of in- terest. The information captured by another sensor from an- other perspective could solve such occluded situations. Fur- thermore, the capabilities to detect and classify various ob- jects in the surrounding can be improved by taking multiple views into account. In order to combine the data of two sensors into one co- ordinate system, a rigid transformation matrix has to be de- rived. The accuracy of modern e.g. satellite based relative pose estimation systems is not sufficient to guarantee a suit- able alignment. Therefore, a registration based approach is used in this work which aligns the captured environment data of two sensors from different positions. Thus their relative pose estimation obtained by traditional methods is improved and the data can be fused. To support this we present an approach which utilizes the uncertainty information of modern tracking systems to de- termine the possible field of view of the other sensor. Fur- thermore, it is estimated which parts of the captured data is directly visible to both, taking occlusion and shadowing ef- fects into account. Afterwards a registration method, based on the iterative closest point (ICP) algorithm, is applied to that data in order to get an accurate alignment. The contribution of the presented approch to the achiev- able accuracy is shown with the help of ground truth data from a LiDAR simulation within a 3-D crossroad model. Re- sults show that a two dimensional position and heading esti- mation is sufficient to initialize a successful 3-D registration process. Furthermore it is shown which initial spatial align- ment is necessary to obtain suitable registration results.

Die Kartierung und Lokalisierung eines mobilen Roboters in seiner Umgebung ist eine wichtige Voraussetzung für dessen Autonomie. In dieser Arbeit wird der Einsatz eines 3D-Laserscanners zur Erfüllung dieser Aufgaben untersucht. Durch die optimierte Anordnung eines rotierenden 2D-Laserscanners werden hochauflösende Bereiche vorgegeben. Zudem wird mit Hilfe von ICP die Kartierung und Lokalisierung im Stillstand durchgeführt. Bei der Betrachtung zur Verbesserung der Bewegungsschätzung wird auch eine Möglichkeit zur Lokalisierung während der Bewegung mit 3D-Scans vorgestellt. Die vorgestellten Algorithmen werden durch Experimente mit realer Hardware evaluiert.