Kamerabasierte Egomotion-Bestimmung mit natürlichen Merkmalen zur Unterstützung von Augmented-Reality-Systemen

In dieser Arbeit werden Verfahren zur Eigenbewegungsschätzung mit Stereokamerasystemen und Tiefenbildkameras untersucht. Der erste Teil beschäftigt sich mit Merkmalsextraktion und -Verfolgung in Bildsequenzen zum Gebrauch in Augmented-Reality-Anwendungen. Im zweiten Teil werden Anwendungsgebiete und Verfahren aus dem Bereich der Stereo-Egomotion analysiert und ein eigener Ansatz, der sowohl mit Stereobildsequenzen als auch mit Tiefenbildsequenzen zurechtkommt, vorgestellt

Multi-Parameter Estimation of Average Speed in Road Networks Using Fuzzy Control

Average speed is crucial for calculating link travel time to find the fastest path in a road network. However, readily available data sources like OpenStreetMap (OSM) often lack information about the average speed of a road. However, OSM contains other road information which enables an estimation of average speed in rural regions. In this paper, we develop a Fuzzy Framework for Speed Estimation (Fuzzy-FSE) that employs fuzzy control to estimate average speed based on the parameters road class, road slope, road surface and link length. The OSM road network and, optionally, a digital elevation model (DEM) serve as free-to-use and worldwide available input data. The Fuzzy-FSE consists of two parts: (a) a rule and knowledge base which decides on the output membership functions and (b) multiple Fuzzy Control Systems which calculate the output average speeds. The Fuzzy-FSE is applied exemplary and evaluated for the BioBío and Maule region in central Chile and for the north of New South Wales in Australia. Results demonstrate that, even using only OSM data, the Fuzzy-FSE performs better than existing methods such as fixed speed profiles. Compared to these methods, the Fuzzy-FSE improves the speed estimation between 2% to 12%. In future work, we will investigate the potential of data-driven machine learning methods to estimate average speed. The applied datasets and the source code of the Fuzzy-FSE are available via GitHu

Efficient 3D Mapping and Modelling of Indoor Scenes with the Microsoft HoloLens: A Survey

The Microsoft HoloLens is a head-worn mobile augmented reality device. It allows a real-time 3D mapping of its direct environment and a self-localisation within the acquired 3D data. Both aspects are essential for robustly augmenting the local environment around the user with virtual contents and for the robust interaction of the user with virtual objects. Although not primarily designed as an indoor mapping device, the Microsoft HoloLens has a high potential for an efficient and comfortable mapping of both room-scale and building-scale indoor environments. In this paper, we provide a survey on the capabilities of the Microsoft HoloLens (Version 1) for the efficient 3D mapping and modelling of indoor scenes. More specifically, we focus on its capabilities regarding the localisation (in terms of pose estimation) within indoor environments and the spatial mapping of indoor environments. While the Microsoft HoloLens can certainly not compete in providing highly accurate 3D data like laser scanners, we demonstrate that the acquired data provides sufficient accuracy for a subsequent standard rule-based reconstruction of a semantically enriched and topologically correct model of an indoor scene from the acquired data. Furthermore, we provide a discussion with respect to the robustness of standard handcrafted geometric features extracted from data acquired with the Microsoft HoloLens and typically used for a subsequent learning-based semantic segmentation

Pose Normalization of Indoor Mapping Datasets Partially Compliant with the Manhattan World Assumption

In this paper, we present a novel pose normalization method for indoor mapping point clouds and triangle meshes that is robust against large fractions of the indoor mapping geometries deviating from an ideal Manhattan World structure. In the case of building structures that contain multiple Manhattan World systems, the dominant Manhattan World structure supported by the largest fraction of geometries is determined and used for alignment. In a first step, a vertical alignment orienting a chosen axis to be orthogonal to horizontal floor and ceiling surfaces is conducted. Subsequently, a rotation around the resulting vertical axis is determined that aligns the dataset horizontally with the coordinate axes. The proposed method is evaluated quantitatively against several publicly available indoor mapping datasets. Our implementation of the proposed procedure along with code for reproducing the evaluation will be made available to the public upon acceptance for publication

Evaluation of HoloLens Tracking and Depth Sensing for Indoor Mapping Applications

The Microsoft HoloLens is a head-worn mobile augmented reality device that is capable of mapping its direct environment in real-time as triangle meshes and localize itself within these three-dimensional meshes simultaneously. The device is equipped with a variety of sensors including four tracking cameras and a time-of-flight (ToF) range camera. Sensor images and their poses estimated by the built-in tracking system can be accessed by the user. This makes the HoloLens potentially interesting as an indoor mapping device. In this paper, we introduce the different sensors of the device and evaluate the complete system in respect of the task of mapping indoor environments. The overall quality of such a system depends mainly on the quality of the depth sensor together with its associated pose derived from the tracking system. For this purpose, we first evaluate the performance of the HoloLens depth sensor and its tracking system separately. Finally, we evaluate the overall system regarding its capability for mapping multi-room environments

Evaluation of Topological Consistency in CityGML

Boundary representation models are data models that represent the topology of a building or city model. This leads to an issue in combination with geometry, as the geometric model necessarily has an underlying topology. In order to allow topological queries to rely on the incidence graph only, a new notion of topological consistency is introduced that captures possible topological differences between the incidence graph and the topology coming from geometry. Intersection matrices then describe possible types of topological consistency and inconsistency. As an application, it is examined which matrices can occur as intersection matrices, and how matrices from topologically consistent data look. The analysis of CityGML data sets stored in a spatial database system then shows that many real-world data sets contain many topologically inconsistent pairs of polygons. It was observed that even if data satisfy the val3dity test, they can still be topologically inconsistent. On the other hand, it is shown that the ISO 19107 standard is equivalent to our notion of topological consistency. In the case when the intersection is a point, topological inconsistency occurs because a vertex lies on a line segment. However, the most frequent topological inconsistencies seem to arise when the intersection of two polygons is a line segment. Consequently, topological queries in present CityGML data cannot rely on the incidence graph only, but must always make costly geometric computations if correct results are to be expected

Combining independent visualization and tracking systems for augmented reality

The basic requirement for the successful deployment of a mobile augmented reality application is a reliable tracking system with high accuracy. Recently, a helmet-based inside-out tracking system which meets this demand has been proposed for self-localization in buildings. To realize an augmented reality application based on this tracking system, a display has to be added for visualization purposes. Therefore, the relative pose of this visualization platform with respect to the helmet has to be tracked. In the case of hand-held visualization platforms like smartphones or tablets, this can be achieved by means of image-based tracking methods like marker-based or model-based tracking. In this paper, we present two marker-based methods for tracking the relative pose between the helmet-based tracking system and a tablet-based visualization system. Both methods were implemented and comparatively evaluated in terms of tracking accuracy. Our results show that mobile inside-out tracking systems without integrated displays can easily be supplemented with a hand-held tablet as visualization device for augmented reality purposes

Collaborative multi-scale 3D city and infrastructure modeling and simulation

Computer-aided collaborative and multi-scale 3D planning are challenges for complex railway and subway track infrastructure projects in the built environment. Many legal, economic, environmental, and structural requirements have to be taken into account. The stringent use of 3D models in the different phases of the planning process facilitates communication and collaboration between the stake holders such as civil engineers, geological engineers, and decision makers. This paper presents concepts, developments, and experiences gained by an interdisciplinary research group coming from civil engineering informatics and geo-informatics banding together skills of both, the Building Information Modeling and the 3D GIS world. New approaches including the development of a collaborative platform and 3D multi-scale modelling are proposed for collaborative planning and simulation to improve the digital 3D planning of subway tracks and other infrastructures. Experiences during this research and lessons learned are presented as well as an outlook on future research focusing on Building Information Modeling and 3D GIS applications for cities of the future

3D Indoor Mapping with the Microsoft HoloLens: Qualitative and Quantitative Evaluation by Means of Geometric Features

3D indoor mapping and scene understanding have seen tremendous progress in recent years due to the rapid development of sensorsystems, reconstruction techniques and semantic segmentation approaches. However, the quality of the acquired data stronglyinfluences the accuracy of both reconstruction and segmentation. In this paper, we direct our attention to the evaluation of themapping capabilities of the Microsoft HoloLens in comparison to high-quality TLS systems with respect to 3D indoor mapping,feature extraction and semantic segmentation. We demonstrate how a set of rather interpretable low-level geometric features andthe resulting semantic segmentation achieved with a Random Forest classifier applied on these features are affected by the qualityof the acquired data. The achieved results indicate that, while allowing for a fast acquisition of room geometries, the HoloLensprovides data with sufficient accuracy for a wide range of applications