Sr Surface Enrichment in Solid Oxide Cells – Approaching the Limits of EDX Analysis by Multivariate Statistical Analysis and Simulations

In solid oxide cells, Sr segregation has been correlated with degradation. Yet, the atomistic mechanism remains unknown. Here we begin to localize the origin of Sr surface nucleation by combining force field based simulations, energy dispersive X-ray spectroscopy (EDX), and multi-variate statistical analysis. We find increased ion mobility in the complexion between yttria-stabilized zirconia and strontium-doped lanthanum manganite. Furthermore, we developed a robust and automated routine to detect localized nucleation seeds of Sr at the complexion surface. This hints at a mechanism originating at the complexion and requires in-depth studies at the atomistic level, where the developed routine can be beneficial for analyzing large hyperspectral EDX datasets

Unifying terrain awareness for the visually impaired through real-time semantic segmentation.

Navigational assistance aims to help visually-impaired people to ambulate the environment safely and independently. This topic becomes challenging as it requires detecting a wide variety of scenes to provide higher level assistive awareness. Vision-based technologies with monocular detectors or depth sensors have sprung up within several years of research. These separate approaches have achieved remarkable results with relatively low processing time and have improved the mobility of impaired people to a large extent. However, running all detectors jointly increases the latency and burdens the computational resources. In this paper, we put forward seizing pixel-wise semantic segmentation to cover navigation-related perception needs in a unified way. This is critical not only for the terrain awareness regarding traversable areas, sidewalks, stairs and water hazards, but also for the avoidance of short-range obstacles, fast-approaching pedestrians and vehicles. The core of our unification proposal is a deep architecture, aimed at attaining efficient semantic understanding. We have integrated the approach in a wearable navigation system by incorporating robust depth segmentation. A comprehensive set of experiments prove the qualified accuracy over state-of-the-art methods while maintaining real-time speed. We also present a closed-loop field test involving real visually-impaired users, demonstrating the effectivity and versatility of the assistive framework

A New Wave in Robotics: Survey on Recent mmWave Radar Applications in Robotics

We survey the current state of millimeterwave (mmWave) radar applications in robotics with a focus on unique capabilities, and discuss future opportunities based on the state of the art. Frequency Modulated Continuous Wave (FMCW) mmWave radars operating in the 76--81GHz range are an appealing alternative to lidars, cameras and other sensors operating in the near visual spectrum. Radar has been made more widely available in new packaging classes, more convenient for robotics and its longer wavelengths have the ability to bypass visual clutter such as fog, dust, and smoke. We begin by covering radar principles as they relate to robotics. We then review the relevant new research across a broad spectrum of robotics applications beginning with motion estimation, localization, and mapping. We then cover object detection and classification, and then close with an analysis of current datasets and calibration techniques that provide entry points into radar research.Comment: 19 Pages, 11 Figures, 2 Tables, TRO Submission pendin

From the semantic point cloud to heritage-building information modeling: A semiautomatic approach exploiting machine learning

This work presents a semi-automatic approach to the 3D reconstruction of Heritage-Building Information Models from point clouds based on machine learning techniques. The use of digital information systems leveraging on three-dimensional (3D) representations in architectural heritage documentation and analysis is ever increasing. For the creation of such repositories, reality-based surveying techniques, such as photogrammetry and laser scanning, allow the fast collection of reliable digital replicas of the study objects in the form of point clouds. Besides, their output is raw and unstructured, and the transition to intelligible and semantic 3D representations is still a scarcely automated and time-consuming process requiring considerable human intervention. More refined methods for 3D data interpretation of heritage point clouds are therefore sought after. In tackling these issues, the proposed approach relies on (i) the application of machine learning techniques to semantically label 3D heritage data by identification of relevant geometric, radiometric and intensity features, and (ii) the use of the annotated data to streamline the construction of Heritage-Building Information Modeling (H-BIM) systems, where purely geometric information derived from surveying is associated with semantic descriptors on heritage documentation and management. The “Grand-Ducal Cloister” dataset, related to the emblematic case study of the Pisa Charterhouse, is discussed

Object Segmentation and Reconstruction Using Infrastructure Sensor Nodes for Autonomous Mobility

This thesis focuses on the Lidar point cloud processing for the infrastructure sensor node that serves as the perception system for autonomous robots with general mobility in indoor applications. Compared with typical schemes mounting sensors on the robots, the method acquires data from infrastructure sensor nodes, providing a more comprehensive view of the environment, which benefits the robot's navigation. The number of sensors would not need to be increased even for multiple robots, significantly reducing costs. In addition, with a central perception system using the infrastructure sensor nodes navigating every robot, a more comprehensive understanding of the current environment and all the robots' locations can be obtained for the control and operation of the autonomous robots. For a robot in the detection range of the sensor node, the sensor node can detect and segment obstacles in its driveable area and reconstruct the incomplete, sparse point cloud of objects upon their movement. The complete shape by the reconstruction benefits the localization and path planning which follows the perception part of the robot's system. Considering the sparse Lidar data and the variety of object categories in the environment, a model-free scheme is selected for object segmentation. Point segmentation starts with background filtering. Considering the complexity of the indoor environment, a depth-matching-based background removal approach is first proposed. However, later tests imply that the method is adequate but not time-efficient. Therefore, based on the depth matching-based method, a process that only focuses on the drive-able area of the robot is proposed, and the computational complexity is significantly reduced. With optimization, the computation time for processing one frame of data can be greatly increased, from 0.2 second by the first approach to 0.01 second by the second approach. After background filtering, the remaining points for occurring objects are segmented as separate clusters using an object clustering algorithm. With independent clusters of objects, an object tracking algorithm is followed to allocate the point clusters with IDs and arrange the clusters in a time sequence. With a stream of clusters for a specific object in a time sequence, point registration is deployed to aggregate the clusters into a complete shape. And as noticed during the experiment, one of the differences between indoor and outdoor environments is that contact between objects in the indoor environment is much more common. The objects in contact are likely to be segmented as a single cluster by the model-free clustering algorithm, which needs to be avoided in the reconstruction process. Therefore an improvement is made in the tracking algorithm when contact happens. The algorithms in this thesis have been experimentally evaluated and presented

Camera Marker Networks for Pose Estimation and Scene Understanding in Construction Automation and Robotics.

The construction industry faces challenges that include high workplace injuries and fatalities, stagnant productivity, and skill shortage. Automation and Robotics in Construction (ARC) has been proposed in the literature as a potential solution that makes machinery easier to collaborate with, facilitates better decision-making, or enables autonomous behavior. However, there are two primary technical challenges in ARC: 1) unstructured and featureless environments; and 2) differences between the as-designed and the as-built. It is therefore impossible to directly replicate conventional automation methods adopted in industries such as manufacturing on construction sites. In particular, two fundamental problems, pose estimation and scene understanding, must be addressed to realize the full potential of ARC. This dissertation proposes a pose estimation and scene understanding framework that addresses the identified research gaps by exploiting cameras, markers, and planar structures to mitigate the identified technical challenges. A fast plane extraction algorithm is developed for efficient modeling and understanding of built environments. A marker registration algorithm is designed for robust, accurate, cost-efficient, and rapidly reconfigurable pose estimation in unstructured and featureless environments. Camera marker networks are then established for unified and systematic design, estimation, and uncertainty analysis in larger scale applications. The proposed algorithms' efficiency has been validated through comprehensive experiments. Specifically, the speed, accuracy and robustness of the fast plane extraction and the marker registration have been demonstrated to be superior to existing state-of-the-art algorithms. These algorithms have also been implemented in two groups of ARC applications to demonstrate the proposed framework's effectiveness, wherein the applications themselves have significant social and economic value. The first group is related to in-situ robotic machinery, including an autonomous manipulator for assembling digital architecture designs on construction sites to help improve productivity and quality; and an intelligent guidance and monitoring system for articulated machinery such as excavators to help improve safety. The second group emphasizes human-machine interaction to make ARC more effective, including a mobile Building Information Modeling and way-finding platform with discrete location recognition to increase indoor facility management efficiency; and a 3D scanning and modeling solution for rapid and cost-efficient dimension checking and concise as-built modeling.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113481/1/cforrest_1.pd

Multi-Object Tracking System based on LiDAR and RADAR for Intelligent Vehicles applications

El presente Trabajo Fin de Grado tiene como objetivo el desarrollo de un Sistema de Detección y Multi-Object Tracking 3D basado en la fusión sensorial de LiDAR y RADAR para aplicaciones de conducción autónoma basándose en algoritmos tradicionales de Machine Learning. La implementación realizada está basada en Python, ROS y cumple requerimientos de tiempo real. En la etapa de detección de objetos se utiliza el algoritmo de segmentación del plano RANSAC, para una posterior extracción de Bounding Boxes mediante DBSCAN. Una Late Sensor Fusion mediante Intersection over Union 3D y un sistema de tracking BEV-SORT completan la arquitectura propuesta.This Final Degree Project aims to develop a 3D Multi-Object Tracking and Detection System based on the Sensor Fusion of LiDAR and RADAR for autonomous driving applications based on traditional Machine Learning algorithms. The implementation is based on Python, ROS and complies with real-time requirements. In the Object Detection stage, the RANSAC plane segmentation algorithm is used, for a subsequent extraction of Bounding Boxes using DBSCAN. A Late Sensor Fusion using Intersection over Union 3D and a BEV-SORT tracking system complete the proposed architecture.Grado en Ingeniería en Electrónica y Automática Industria

Mobile graphics: SIGGRAPH Asia 2017 course

Peer ReviewedPostprint (published version

Assessing wood properties in standing timber with laser scanning

Managed forests play crucial roles in ongoing climatic and environmental changes. Among other things, wood is capable of sinking and storing carbon in both standing timber and wood products. To promote these positive effects, more precise planning is required that will ensure sustainable forest management and maximal deposition of harvested wood for long-term applications. Information on wood properties plays a key role; i.e. the wood properties can impact the carbon stocks in forests and the suitability of wood for structural timber. With respect to the theoretical background of wood formation, stem, crown, and branching constitute potential inputs (i.e. wood quality indicators) to allometric wood property, tree biomass, and wood quality models. Due to the complex nature of wood formation, measurements of wood quality indicators that could predict wood properties along the relevant directions of variation have previously been elusive in forest inventories. However, developments in laser scanning from aerial and terrestrial platforms support more complex mapping and modeling regimes based on dense three-dimensional point clouds. The aim here was to determine how wood properties could be estimated in remote-sensing-aided forest inventories. For this purpose, methods for characterizing select wood quality indicators in standing timber, using airborne and terrestrial laser scanning (ALS and TLS, respectively) were developed and evaluated in managed boreal Scots pine (Pinus sylvestris L.) forests. Firstly, the accuracies of wood quality indicators resolved from TLS point clouds were assessed. Secondly, the results were compared with x-ray tomographic references from sawmills. Thirdly, the accuracies of tree-specific crown features delineated from the ALS data in predictive modeling of the wood quality indicators were evaluated. The results showed that the quality and density of point clouds significantly impacted the accuracies of the extracted wood quality indicators. In the assessment of wood properties, TLS should be considered as a tool for retrieving as dense stem and branching data as possible from carefully selected sample trees. Accurately retrieved morphological data could be applied to allometric wood property models. The models should use tree traits predictable with aerial remote sensing (e.g. tree height, crown dimensions) to enable extrapolations. As an outlook, terrestrial and aerial remote sensing can play an important role in filling in the knowledge gaps regarding the behavior of wood properties over different spatial and temporal extents. Further interdisciplinary cooperation will be needed to fully facilitate the use of remote sensing and spatially transferable wood property models that could become useful in tackling the challenges associated with changing climate, silviculture, and demand for wood.Hoidetuilla metsillä on useita tärkeitä rooleja muuttuvassa ilmastossa ja ympäristössä. Puu sitoo ja varastoi hiiltä niin kasvaessaan, kuin pitkäikäisiksi puutuotteiksi jalostettuna. Näiden vaikutusten huomioiminen metsänhoidossa vaatii tarkkaa suunnittelua, jolla varmistetaan metsänhoidon ja puunkäytön kestävyys. Tieto puuaineen ominaisuuksista on keskeisessä osassa, sillä ne vaikuttavat hiilivarastojen suuruuteen metsissä, sekä puun käytettävyyteen pitkäikäisenä rakennesahatavarana. Puunmuodostuksen teoreettisen taustan mukaisesti, runko, latvus ja oksarakenne ovat potentiaalisia selittäviä muuttujia (eli puun laatuindikaattoreita), kun mallinnetaan puuaineen ominaisuuksia, puubiomassaa ja puun laatua. Puunmuodostuksen monimutkaisuudesta ja moniulotteisesta vaihtelusta johtuen, tarvittavien laatuidikaattorien mittaaminen osana metsävarojen inventointia ja riittävällä yksityiskohtaisuudella on ollut aiemmin mahdotonta. Monialustaisen laserkeilauksen kehittyminen kuitenkin tukee aiempaa monipuolisempien kartoitus- ja mallinnusjärjestelmien rakentamista, jotka perustuvat tiheisiin kolmiulotteisiin pistepilviin. Tämän työn tavoitteena oli määritellä, kuinka puuaineen ominaisuuksia voidaan arvioida kaukokartoitusta hyödyntävässä metsävarojen inventoinnissa. Tätä tarkoitusta varten kehitettiin menetelmiä puun laatuindikaattorien mittaamiseksi hoidetuissa männiköissä (Pinus sylvestris L.) lento- ja maastolaserkeilauksen avulla, ja arvioitiin niiden toimivuutta. Ensin arvioitiin laatuindikaattorien mittatarkkuus pistepilvissä. Toiseksi verrattiin pistepilvimittauksia röntgentomografiamittauksiin teollisilla sahoilla. Kolmanneksi arvioitiin lentolaserkeilauksella tuotettujen latvuspiirteiden tarkkuutta laatuindikaattorien ennustamisessa. Tuloksien perusteella pistepilvien laatu ja pistetiheys vaikuttivat merkittävästi mitattujen laatuindikaattorien tarkkuuteen. Puuaineen ominaisuuksien arvioimisessa, maastolaserkeilausta tulisi käyttää työkaluna mahdollisimman yksityiskohtaisten runko- ja oksikkuustietojen keräämiseen tarkkaan valikoiduista näytepuista. Tarkasti mitatut laatuindikaattorit voivat selittää puuaineen ominaisuuksia mallinnuksessa. Käytettyjen mallien tulisi perustua laatuindikaattoreille, jotka voidaan ennustaa lentolaserkeilausaineistosta (esim. puun pituus ja latvuksen mittasuhteet), jotta ennusteet ovat yleistettävissä laajoille alueille. Tulevaisuudessa, maasta ja ilmasta tehtävällä kaukokartoituksella voi olla tärkeä rooli puuaineen ominaisuuksien aikaan ja paikkaan sidotun vaihtelun tutkimuksessa. Lisää poikkitieteellistä työtä tarvitaan, jotta kaukokartoitusta ja puuaineen ominaisuuksia ennustavia spatiaalisia malleja voidaan täysimittaisesti hyödyntää kiihtyvän ilmastonmuutoksen, muuttuvan metsänhoidon ja lisääntyvän puunkäytön tuomien haasteiden kohtaamisessa