Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Indoor Mapping and Reconstruction with Mobile Augmented Reality Sensor Systems

Augmented Reality (AR) ermöglicht es, virtuelle, dreidimensionale Inhalte direkt innerhalb der realen Umgebung darzustellen. Anstatt jedoch beliebige virtuelle Objekte an einem willkürlichen Ort anzuzeigen, kann AR Technologie auch genutzt werden, um Geodaten in situ an jenem Ort darzustellen, auf den sich die Daten beziehen. Damit eröffnet AR die Möglichkeit, die reale Welt durch virtuelle, ortbezogene Informationen anzureichern. Im Rahmen der vorliegenen Arbeit wird diese Spielart von AR als "Fused Reality" definiert und eingehend diskutiert. Der praktische Mehrwert, den dieses Konzept der Fused Reality bietet, lässt sich gut am Beispiel seiner Anwendung im Zusammenhang mit digitalen Gebäudemodellen demonstrieren, wo sich gebäudespezifische Informationen - beispielsweise der Verlauf von Leitungen und Kabeln innerhalb der Wände - lagegerecht am realen Objekt darstellen lassen. Um das skizzierte Konzept einer Indoor Fused Reality Anwendung realisieren zu können, müssen einige grundlegende Bedingungen erfüllt sein. So kann ein bestimmtes Gebäude nur dann mit ortsbezogenen Informationen augmentiert werden, wenn von diesem Gebäude ein digitales Modell verfügbar ist. Zwar werden größere Bauprojekt heutzutage oft unter Zuhilfename von Building Information Modelling (BIM) geplant und durchgeführt, sodass ein digitales Modell direkt zusammen mit dem realen Gebäude ensteht, jedoch sind im Falle älterer Bestandsgebäude digitale Modelle meist nicht verfügbar. Ein digitales Modell eines bestehenden Gebäudes manuell zu erstellen, ist zwar möglich, jedoch mit großem Aufwand verbunden. Ist ein passendes Gebäudemodell vorhanden, muss ein AR Gerät außerdem in der Lage sein, die eigene Position und Orientierung im Gebäude relativ zu diesem Modell bestimmen zu können, um Augmentierungen lagegerecht anzeigen zu können. Im Rahmen dieser Arbeit werden diverse Aspekte der angesprochenen Problematik untersucht und diskutiert. Dabei werden zunächst verschiedene Möglichkeiten diskutiert, Indoor-Gebäudegeometrie mittels Sensorsystemen zu erfassen. Anschließend wird eine Untersuchung präsentiert, inwiefern moderne AR Geräte, die in der Regel ebenfalls über eine Vielzahl an Sensoren verfügen, ebenfalls geeignet sind, als Indoor-Mapping-Systeme eingesetzt zu werden. Die resultierenden Indoor Mapping Datensätze können daraufhin genutzt werden, um automatisiert Gebäudemodelle zu rekonstruieren. Zu diesem Zweck wird ein automatisiertes, voxel-basiertes Indoor-Rekonstruktionsverfahren vorgestellt. Dieses wird außerdem auf der Grundlage vierer zu diesem Zweck erfasster Datensätze mit zugehörigen Referenzdaten quantitativ evaluiert. Desweiteren werden verschiedene Möglichkeiten diskutiert, mobile AR Geräte innerhalb eines Gebäudes und des zugehörigen Gebäudemodells zu lokalisieren. In diesem Kontext wird außerdem auch die Evaluierung einer Marker-basierten Indoor-Lokalisierungsmethode präsentiert. Abschließend wird zudem ein neuer Ansatz, Indoor-Mapping Datensätze an den Achsen des Koordinatensystems auszurichten, vorgestellt

Keyframe-based monocular SLAM: design, survey, and future directions

Extensive research in the field of monocular SLAM for the past fifteen years has yielded workable systems that found their way into various applications in robotics and augmented reality. Although filter-based monocular SLAM systems were common at some time, the more efficient keyframe-based solutions are becoming the de facto methodology for building a monocular SLAM system. The objective of this paper is threefold: first, the paper serves as a guideline for people seeking to design their own monocular SLAM according to specific environmental constraints. Second, it presents a survey that covers the various keyframe-based monocular SLAM systems in the literature, detailing the components of their implementation, and critically assessing the specific strategies made in each proposed solution. Third, the paper provides insight into the direction of future research in this field, to address the major limitations still facing monocular SLAM; namely, in the issues of illumination changes, initialization, highly dynamic motion, poorly textured scenes, repetitive textures, map maintenance, and failure recovery

구글 스트릿뷰를 이용한 도시 협곡 내 평균복사온도 추정

학위논문 (석사) -- 서울대학교 대학원 : 농업생명과학대학 생태조경학과, 2021. 2. 이동근.도시개발로 인해 보행자의 에너지 균형을 변화시키며 도시공간의 열 쾌적성이 악화되는 등 열 환경문제가 발생하고 있다. 선행연구에서는 도시 공간 내 열 쾌적성을 정량적으로 평가하기 위해 인간의 가장 중요한 생체 기상 변수 중 하나인 평균복사온도를 산정하는 연구가 진행되고 있다. 하지만 산정식이 복잡하거나, 넓은 범위에서의 공간 데이터 취득이 어렵기 때문에, 커뮤니티 단위에서 고해상도의 평균복사온도를 추정하는 것은 어렵다. 따라서 본 연구에서는 구글스트릿뷰 이미지를 사용하여 도시 거리 협곡내 평균복사 온도를 추정하는 방법을 제시하고, 도시 스케일에서 도시열섬 분석을 위해 많은 연구가 진행된 지표면 온도와 평균복사온도간 관계를 공간패턴 측면에서 분석하였다. 우선 평균복사온도 추정식에 큰 영향을 미치는 천공률은 파노라마 이미지를 바탕으로 딥러닝을 활용하여 도시 요인별(건물, 나무, 하늘 등)분류하고, 어안렌즈 이미지로 변환하여 도출하였다. 또한 어안렌즈 이미지를 중심으로 태양경로 알고리즘을 활용하여 시간별 그림자의 유무를 판단하였다. 마지막으로 기후요인, 시간, 위치 등 데이터를 활용하여 장파, 단파 복사를 도출하여 평균복사온도를 산정하였다. 제안된 평균복사온도 추정 방법과 실측간 비교(7 곳) 결과 단파, 장파 값의 R^2값이 각각 0.97, 0.77로 나타났다. 다른 모델과 비교한 결과, 높은 정확도를 확인할 수 있으며 복잡한 도시 환경에서의 활용가능성을 확인할 수 있다. 도시규모에서 지표면온도, 평균복사온도를 공간패턴 측면에서 비교한 결과 천공률, 빌딩 뷰팩터가 각각 0.6~1.0, 0.35-0.5인 오픈스페이스 혹은 저층 밀집지역에서 높은 평균복사온도(>59.4°C)를 보였다. 반면 높은 빌딩이 밀집된 지역의 경우(빌딩 뷰팩터 :0.4-0.6, 나무 뷰팩터 0.6-0.9) 낮은 평균복사온도(<47.6°C)를 보였다. 특히 거리의 방향이 동-서 인 경우에는 천공률이 0.3-0.55 일지라도 높은 평균복사온도를 확인할 수 있었다. 추가적으로 평균복사온도와 지표면 온도간 비교결과 전반적으로 높은 온도 값을 가진 공간이 유사하였으나, 저층 고밀도 건물 지역 혹은 초지 지역에서 상반된 결과를 확인할 수 있었다. 본 연구에서는 도시스케일에서 높은 해상도로 평균복사온도를 추정하는 방법을 딥러닝을 활용하여 제시하였으며, 지표면 온도와 공간패턴별 분석을 통해 실제 보행자가 체감하는 열 환경을 개선하기 위한 방안을 제시할 수 있는 기초자료를 제공하였다. 이는 도시 열 환경을 고려한 지속가능한 도시 공간 설계 및 환경 계획 측면에서 활용 될 수 있으며, 특히 공간데이터 취득이 어려운 곳에서의 높은 활용성을 기대해 볼 수 있다.This paper presents a method for estimating Mean Radiant Temperature (MRT) of street canyons using Google Street View (GSV) images and investigates its spatial patterns in street-level on large scale. We used image segmentation using deep learning, project panorama to fisheye image and sun path algorithms to estimate MRT using GSV. Verification of proposed method can be explained by total of 7 field measurements in clear-sky of street-level, since the estimated shortwave and longwave radiation of value is 0.97, 0.77 respectively. The method proposed in this study is suitable for actual complex urban environment consisting of buildings, tree and streets. Additionally, we compared calculated MRT and LST (Land Surface Temperature) from Landsat 8 in a city scale. As a result of investigating spatial patterns of MRT in Seoul, We found that Higher MRT of street canyons ( >59.4℃) is mainly distributed in open space areas and compact low-rise density building where SVF (Sky View Factor) is 0.6–1.0 and BVF(Building View Factor) is 0.35–0.5, or West-East orientation street canyons with SVF(0.3–0.55). On the other hand, high density building (BVF is 0.4–0.6) or high density tree areas (TVF (Tree View Factor) is 0.6–0.99) showed Low MRT ( < 47.6). The mapped MRT results had similar spatial distribution with LST, but the MRT(?) lower (?) than LST in low tree density or low-rise high-density building areas. And it will help decision makers how to improve thermal comfort at the street-level.Chapter 1. Introduction １ 1.1. Study Background １ 1.2. Literature review ４ 1.2.1 Mean radiant temperature formula ４ 1.2.2 Surface temperature simulation model ５ Chapter 2. Study area and data １０ 2.1. Study area １０ 2.2. Data collection １１ Chapter 3. Method １３ 3.1. Research flow １３ 3.2. MRT simulation １４ 3.2.1. Schematic flow for MRT simulation １４ 3.2.2. Urban canyon geometry calculation using GSV images (Phase I: built geometry data) １６ 3.2.3. Street canyon solar radiation calculation (Phase II:radiation transfer calculation.) １７ 3.2.3.1 Calculation of street-level shortwave radiation １７ 3.2.3.2 Calculation of street-level long-wave radiation １９ 3.2.4. Phase III mean radiation temperature calculation ２１ Chapter 4. Result and Discussion ２２ 4.1. verification of solar radiation estimated in street-level ２２ 4.2. Validation of Long-wave radiation ２４ 4.3. Comparison between LST and MRT estimated using GSV ２６ 4.4. Comparison of GSV_MRT with other models ２９ 4.5. limitations and future development ３２ Chapter 5. Conclusion ３４ Bibliography ３６ Abstract in Korean ４３ Appendix ４５Maste

Augmented Reality

Augmented Reality (AR) is a natural development from virtual reality (VR), which was developed several decades earlier. AR complements VR in many ways. Due to the advantages of the user being able to see both the real and virtual objects simultaneously, AR is far more intuitive, but it's not completely detached from human factors and other restrictions. AR doesn't consume as much time and effort in the applications because it's not required to construct the entire virtual scene and the environment. In this book, several new and emerging application areas of AR are presented and divided into three sections. The first section contains applications in outdoor and mobile AR, such as construction, restoration, security and surveillance. The second section deals with AR in medical, biological, and human bodies. The third and final section contains a number of new and useful applications in daily living and learning