Registering partial shapes plays an important role in numerous applications in the fields of robotics, vision, and graphics. An essential problem of registration algorithms is the determination of correspondences between surfaces. In this paper, we provide a in-depth evaluation of an approach that computes high-quality correspondences for pair-wise closest point-based iterative registration and compare the results with state-of-the-art registration algorithms. Instead of using a discrete point set for correspondence search, the approach is based on a locally reconstructed continuous moving least squares surface to overcome sampling mismatches in the input shapes. Furthermore, MLS-based correspondences are highly robust to noise. We demonstrate that this strategy outperforms existing approaches in terms of registration accuracy by combining it with the SparseICP local registration algorithm. Our extensive evaluation over several thousand scans from different sources verify that MLS-based approach results in a significant increase in alignment accuracy, surpassing state-of-theart feature-based and probabilistic methods. At the same time, it allows an efficient implementation that introduces only a modest computational overhead.

Notre planète est pour l’essentiel recouverte par les mers et les océans, or notre connaissance des fonds marins est très inférieure à celle que nous possédons sur les terres émergées. Dans ce mémoire, nous cherchons à concevoir un système dédié à la cartographie thématique à grande échelle pour obtenir à la demande un nuage de points dense représentatif d’une scène sous-marine ou subaquatique par reconstruction tridimensionnelle. Le caractère complexe de ce type de système nous amène à privilégier une approche délibérément transversale. Nous nous intéresserons en particulier aux problématiques posées par l’étude à l’échelle des individus de petites zones en eau peu profonde. Les premières concernent l’acquisition in situ efficace de couples stéréoscopiques avec une logistique adaptée à la taille des zones observées : nous proposons pour cela un microsystème agile, peu coûteux et suffisamment automatisé pour fournir des données reproductibles et comparables. Les secondes portent sur l’extraction fiable de l’information tridimensionnelle à partir des données acquises : nous exposons les algorithmes que nous avons élaborés pour prendre en compte les caractéristiques particulières du milieu aquatique (dynamisme, propagation difficile des ondes électromagnétiques, etc.). Nous abordons donc en détail dans ce mémoire les problèmes d’appariement dense, d’étalonnage, d’acquisition in situ, de recalage et de redondance des données rencontrés dans le milieu sous-marin. / Our planet is mostly covered by seas and oceans. However, our knowledge of the seabed is far more restricted than that of land surface. In this thesis, we seek to develop a system dedicated to precise thematic mapping to obtain a dense point cloud of an underwater area on demand by using three-dimensional reconstruction. The complex nature of this type of system leads us to favor a multidisciplinary approach. We will examine in particular the issues raised by studying small shallow water areas on the scale of individual objects. The first problems concern the effective in situ acquisition of stereo pairs with logistics adapted to the sizes of the observed areas: for this, we propose an agile, affordable microsystem which is sufficiently automated to provide reproducible and comparable data. The second set of problems relates to the reliable extraction of three-dimensional information from the acquired data: we outline the algorithms we have developed to take into account the particular characteristics of the aquatic environment (such as its dynamics or its light absorption). We therefore discuss in detail the issues encountered in the underwater environment concerning dense matching, calibration, in situ acquisition, data registration and redundancy.

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The thesis investigates above-ground biomass (AGB) with terrestrial laser scanning (TLS) for estimating AGB in a study area in Valls Hage, Gävle. The study used TLS for field measurements to collect highly detailed point clouds of two tree species for AGB estimation and comparison against validation data. TLS-derived data were validated using a non-destructive method involving direct field measurements using tape measures and a Trimble SX12 for extracting diameter at breast height (DBH), tree height, and crown diameter. Wood density was obtained from the literature. Data processing for segmentation, filtering, and generation of the quantitative structure model (QSM) was performed by using SimpleForest tool in Computree software. A statistical analysis was performed using linear regression, and AGB was estimated using QSM-derived volume multiplied by wood density. The finding in the results for the comparison of AGB estimation between TLS QSM and field validation from DBH-based tree-specific allometric equation had an RMSE of 154 kg, with a near-perfect agreement of 0.997 %, and RMSE of 189 kg, with the agreement of 0.990% for TLS QSM and TLS validation DBH-based tree specific equation. The comparison between TLS-derived DBH and field validation was accurate, leaving with insignificant differences, while the tree height had noticeable differences, and crown diameter had relatively low differences. The challenges during data processing were highlighted and the importance of TLS data for accurate AGB estimation, with the potential for refinement and integrating internal tree structure information to improve allometric models for future studies.

Malfunction in the overhead contact line system is a common cause of disturbances in the train traffic in Sweden. Due to the preventive methods being inefficient, the Swedish Transport Administration has stated the need to develop the railroad maintenance services and has identified Artificial Intelligence (AI) as an important tool for this undertaking.  Light Detection and Ranging (LiDAR) is a remote sensing technology that has been gaining popularity in recent years due to its high ranging accuracy and decreasing data acquisition cost. LiDAR is commonly used within the railroad industry and companies such as WSP collects large amount of data through LiDAR measurements every year. There is currently no reliable fully automatic method to process the point cloud data structure. Several studies propose innovative methods based on traditional machine learning to extract railroad system components from point clouds and have been able to do so with good results. However, these methods have limited applicability in real world problems, as they build upon hand-crafted features based on previous knowledge of the data on which they are applied. Deep learning technology may be a better alternative for the task as it does not require the same amount of human interaction for feature engineering and knowledge about the data in advance.  This thesis investigates if contact line poles can be recognized from LiDAR data with the use of the neural network architecture DGCNN. Data from two Swedish railroad lines, Saltsjöbanan and Roslagsbanan, provided by WSP was used. Point labels were predicted through semantic segmentation from which objects were distinguished using the clustering algorithm DBSCAN. The network was trained and validated on Saltsjöbanan using k-fold cross-validation and was later tested on Roslagsbanan to simulate the application of trained models on an unknown dataset. On point level the network achieved an estimated precision of 0.87 and a recall of 0.89 on the data from Saltsjöbanan and an estimated precision of 0.92 and recall of 0.83 on the data from Roslagsbanan. In the object recognition task, the approach achieved an average precision of 0.93 and recall of 0.998 on the data from Saltsjöbanan and on the data from Roslagsbanan, an average precision of 0.96 and a recall of 1 was achieved, indicating that it is possible to apply this method on railroad segments other than the one the network was trained on. Despite not being accurate or reliable enough on point level to be used for thorough inspection of the contact line system, this approach has various applications in terms of object recognition along Swedish railroads. Future research should investigate how adding additional classes beyond contact line poles would affect the results and what changes can be done to the parameters to optimize the performance. A side-by-side comparison with the current methods and traditional machine learning-based methods would be valuable as well. / Fel i kontaktledningssystemet är en vanlig orsak till störningar i tågtrafiken i Sverige. Då dagens metoder för att förebygga dessa fel är ineffektiva har Trafikverket uttryckt behovet av att utveckla underhållsarbetet av den svenska järnvägen och har identifierat artificiell intelligens (AI) som ett viktigt verktyg i det syftet. Light Detection and Ranging (LiDAR) är en fjärranalysteknologi som har blivit allt mer populär med åren tack vare sin höga mätnoggrannheten och allt billigare datainsamling. LiDAR används regelbundet inom järnvägsindustrin och företag som WSP samlar årligen in stora mängder data med denna teknologi. I dagsläget finns det däremot ingen tillräckligt pålitlig automatisk metod för att segmentera och klassificera punktmoln. Ett flertal studier föreslår lösningar baserade på traditionell maskininlärning för att ta ut järnvägskomponenter ur punktmolnsdata. Eftersom dessa metoder bygger på förkunskap och noga utvecklade funktioner för att hitta mönster i datan är de svåra att tillämpa i verkliga problem. Istället kan djupinlärning som inte kräver samma förkunskap eller noggranna matematiska modellering tillämpas. I det här arbetet identifierades kontaktledningsstolpar ur LiDAR data med hjälp av det neurala nätverket DGCNN. Datan som användes var punktmolnsdata från Saltsjöbanan och Roslagsbanan försedd av WSP. Först klassificerades punkter genom semantisk segmentering och från klassificeringen kunde objekt identifierades genom att tillämpa klusteringsalgoritmen DBSCAN. Nätverket tränades med hjälp av korsvalidering på data över Saltsjöbanan och testades därefter på data över Roslagsbanan för att undersöka om tränade modeller kan tillämpas på andra järnvägslinjer. På datan över Saltsjöbanan uppnådde nätverket en estimerad specificitet på 0.87 och sensitivitet på 0.89 på punktnivå. Motsvarande värden på datan över Roslagsbanan låg på 0.92 och 0.83. Metoden för objektigenkänning uppnådde en genomsnittlig specificitet på 0.93 och sensitivitet på 0.998 på datan över Saltsjöbanan och motsvarande värden på datan över Roslagsbanan låg på 0.96 och 1. Resultatet indikerar att metoden går att tillämpa på andra järnvägslinjer utan specifik träning för dessa.  Trots att metoden inte är träffsäker nog på punktnivå för att användas för grundlig besiktning av kontaktledningssystemet kan den användas för objektigenkänning längs svensk järnväg. Framtida forskning bör undersöka hur resultatet påverkas om ytterligare klasser utöver kontaktledningsstolpar används och vilka förändringar bör göras bland parametrarna för att optimera det undersökta tillvägagångssättet. En utförlig jämförelse mot nuvarande metoder och metoder baserade på traditionell maskininlärning skulle dessutom vara av värde.

[pt] Esta dissertação tem por objetivo realizar a caracterização metrológica de scanners ópticos tridimensionais por projeção de luz estruturada com vistas à aplicação em ensaios de coletes balísticos. Técnicas de digitalização tridimensional vêm ganhando popularidade nas últimas décadas, entretanto o recente emprego de equipamentos de digitalização 3D em ensaios de coletes balísticos constitui uma nova aplicação para estes equipamentos, em especial na caracterização dos traumas originados pelos impactos de projéteis, o que motivou a realização do estudo. A metodologia empregada fundamentou-se nas pesquisas bibliográfica, documental, experimental e de laboratório que tiveram por objetivo coletar dados utilizando scanner por projeção de luz estruturada no contexto de ensaios de coletes balísticos e compará-los com valores de referência. Foram sugeridos processos de alinhamento, segmentação, filtragem e estabelecimento de planos de referência que se mostraram adequados ao tratamento das nuvens de pontos obtidas nos ensaios de coletes balísticos. Os resultados confirmaram os erros sistemáticos relatados na literatura para equipamentos de digitalização por luz estruturada e possibilitaram uma estimativa da incerteza de medição para o equipamento em questão. Concluiuse que os valores críticos de medição de traumas são corretamente medidos pelo instrumento de digitalização 3D e por meio da utilização do método sugerido para tratamento de nuvens de pontos neste contexto. / [en] This dissertation aims at performing the metrological characterization of three-dimensional optical scanners by structured light projection for application in ballistic vest tests. Three-dimensional scanning techniques have been gaining popularity in recent decades, however the recent use of 3D scanning equipment in ballistic vests testing is a new application for these devices, especially in the characterization of traumas caused by projectile impacts, which motivated the realization of the study. The methodology used was based on bibliographic, documentary, experimental and laboratory research aimed at collecting data using a structured light projection scanner in the context of ballistic vests tests and comparing them with reference values. Procedures for alignment, segmentation, filtering and establishment of reference planes were suggested, which proved to be adequate for the treatment of point clouds obtained from ballistic vest tests. The results confirmed the systematic errors reported in the literature for structured light scanning equipment and made it possible to estimate the measurement uncertainty for the equipment in question. It was concluded that the critical trauma measurement values are correctly measured by the 3D scanning instrument and by using the suggested method for treating point clouds in this context.

Humanity has always wanted to explore the world we live in and answer different questions about our universe. After the International Space Station will end its service one possible next step could be a Moon Outpost: a convenient location for research, astronaut training and technological development that would enable long-duration space. This location can be inside one of the presumed lava tubes that should be present under the surface but would first need to be inspected, possibly by machine capable of capturing and relaying a map to a team on Earth.In this report the past and future Moon base missions will be summarized considering feasible outpost scenarios from the space companies or agencies. and their prospected manned budget. Potential mission profiles, objectives, requirements and constrains of the BATonomous Moon cave Explorer (BAT-ME) mission will be discussed and defined. Vehicle and mission concept will be addressed, comparing and presenting possible propulsion or locomotion approaches inside the lava tube.The Inkonova “Batonomous™” system is capable of providing Simultaneous Localization And Mapping (SLAM), relay the created maps, with the possibility to easily integrate the system on any kind of vehicle that would function in a real-life scenario.Although the system is not fully developed, it will be assessed from a technical perspective, and proper changes for a viable system transition for the space-Moon environment will be devised. The transition of the system from the Batonomous™ state to the BAT-ME required state will be presented from the requirement, hardware, software, electrical and operational point of view.The mission will be devised into operational phases, with key goals in mind. Two different vehicles will be presented and designed on a high engineering level. A risk analysis and management system will be made to understand the possible negative outcomes of different parts failure on the mission outcome.

This method demonstrates an approach to determine the best grasping location on an unknown object using Weighted Random Forest Algorithm. It used RGB-D value of an object as input to find a suitable rectangular grasping region as the output. To accomplish this task, it uses a subspace of most important features from a very high dimensional extensive feature space that contains both image and point cloud features. Usage of most important features in the grasping algorithm has enabled the system to be computationally very fast while preserving maximum information gain. In this approach, the Random Forest operates using optimum parameters e.g. Number of Trees, Number of Features at each node, Information Gain Criteria etc. ensures optimization in learning, with highest possible accuracy in minimum time in an advanced practical setting. The Weighted Random Forest chosen over Support Vector Machine (SVM), Decision Tree and Adaboost for implementation of the grasping system outperforms the stated machine learning algorithms both in training and testing accuracy and other performance estimates. The Grasping System utilizing learning from a score function detects the rectangular grasping region after selecting the top rectangle that has the largest score. The system is implemented and tested in a Baxter Research Robot with Parallel Plate Gripper in action.

Automatic bin picking is a well-known problem in industrial automation and computer vision, where a robot picks an object from a bin and places it somewhere else. There is continuous ongoing research for many years to improve the contemporary solution. With camera technology advancing rapidly and available fast computation resources, solving this problem with deep learning has become a current interest for several researchers. This thesis intends to leverage the current state-of-the-art deep learning based methods of 3D instance segmentation and point cloud registration and combine them to improve the bin picking solution by improving the performance and make them robust. The problem of bin picking becomes complex when the bin contains identical objects with heavy occlusion. To solve this problem, a 3D instance segmentation is performed with Fast Point Cloud Clustering (FPCC) method to detect and locate the objects in the bin. Further, an extraction strategy is proposed to choose one predicted instance at a time. Inthe next step, a point cloud registration technique is implemented based on PointNetLK method to estimate the pose of the selected object from the bin. The above implementation is trained, tested, and evaluated on synthetically generated datasets. The synthetic dataset also contains several noisy point clouds to imitate a real situation. The real data captured at the company ’SICK IVP’ is also tested with the implemented model. It is observed that the 3D instance segmentation can detect and locate the objects available in the bin. In a noisy environment, the performance degrades as the noise level increase. However, the decrease in the performance is found to be not so significant. Point cloud registration is observed to register best with the full point cloud of the object, when compared to point cloud with missing points.