One fundamental task in robotics is random bin-picking, where it is important to be able to detect an object in a bin and estimate its pose to plan the motion of a robotic arm. For this purpose, this thesis work aimed to investigate and evaluate algorithms for 6D pose estimation when the object was given by a CAD model. The scene was given by a point cloud illustrating a partial 3D view of the bin with multiple instances of the object. Two algorithms were thus implemented and evaluated. The first algorithm was an approach based on Point Pair Features, and the second was Fast Global Registration. For evaluation, four different CAD models were used to create synthetic data with ground truth annotations. It was concluded that the Point Pair Feature approach provided a robust localization of objects and can be used for bin-picking. The algorithm appears to be able to handle different types of objects, however, with small limitations when the object has flat surfaces and weak texture or many similar details. The disadvantage with the algorithm was the execution time. Fast Global Registration, on the other hand, did not provide a robust localization of objects and is thus not a good solution for bin-picking.

La numérisation 3D du patrimoine bâti est un procédé qui s’inscrit dans de multiples applications (documentation, visualisation, etc.), et peut tirer profit de la diversité des techniques de mesure disponibles. Afin d’améliorer la complétude et la qualité des livrables, de plus en plus de projets de numérisation s’appuient sur la combinaison de nuages de points provenant de différentes sources. La connaissance des performances propres aux différents capteurs, ainsi que de la qualité de leurs mesures, est alors souhaitable. Par la suite, plusieurs pistes peuvent être explorées en vue d’intégrer des nuages hétérogènes au sein d’un même projet, de leur recalage à la modélisation finale. Une approche pour le recalage simultané de plusieurs nuages de points est exposée dans ces travaux. La gestion de potentielles fautes parmi les observations, ou de bruit de mesure inhérent à certaines techniques de levé, est envisagée à travers l’ajout d’estimateurs robustes dans la méthodologie de recalage. / Three dimensional digitization of built heritage is involved in a wide range of applications (documentation, visualization, etc.), and may take advantage of the diversity of measurement techniques available. In order to improve the completeness as well as the quality of deliverables, more and more digitization projects rely on the combination of data coming from different sensors. To this end, the knowledge of sensor performances along with the quality of the measurements they produce is recommended. Then, different solutions can be investigated to integrate heterogeneous point clouds within a same project, from their registration to the modeling steps. A global approach for the simultaneous registration of multiple point clouds is proposed in this work, where the introduction of individual weights for each dataset is foreseen. Moreover, robust estimators are introduced in the registration framework, in order to deal with potential outliers or measurement noise among the data.

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.

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.

When a Time-of-Flight (ToF) depth camera is used to monitor a region of interest, it has to be mounted correctly and have information regarding its position. Manual configuration currently require managing captured 3D ToF data in a 2D environment, which limits the user and might give rise to errors due to misinterpretation of the data. This thesis investigates if a real time 3D reconstruction mesh from a Microsoft HoloLens can be used as a target for point cloud registration using the ToF data, thus configuring the camera autonomously. Three registration algorithms, Fast Global Registration (FGR), Joint Registration Multiple Point Clouds (JR-MPC) and Prerejective RANSAC, were evaluated for this purpose. It was concluded that despite using different sensors it is possible to perform accurate registration. Also, it was shown that the registration can be done accurately within a reasonable time, compared with the inherent time to perform 3D reconstruction on the Hololens. All algorithms could solve the problem, but it was concluded that FGR provided the most satisfying results, though requiring several constraints on the data.