Real-Time Multi-Fisheye Camera Self-Localization and Egomotion Estimation in Complex Indoor Environments

In this work a real-time capable multi-fisheye camera self-localization and egomotion estimation framework is developed. The thesis covers all aspects ranging from omnidirectional camera calibration to the development of a complete multi-fisheye camera SLAM system based on a generic multi-camera bundle adjustment method

ISAR: Ein Autorensystem für Interaktive Tische

Developing augmented reality systems involves several challenges, that prevent end users and experts from non-technical domains, such as education, to experiment with this technology. In this research we introduce ISAR, an authoring system for augmented reality tabletops targeting users from non-technical domains. ISAR allows non-technical users to create their own interactive tabletop applications and experiment with the use of this technology in domains such as educations, industrial training, and medical rehabilitation.Die Entwicklung von Augmented-Reality-Systemen ist mit mehreren Herausforderungen verbunden, die Endbenutzer und Experten aus nicht-technischen Bereichen, wie z.B. dem Bildungswesen, daran hindern, mit dieser Technologie zu experimentieren. In dieser Forschung stellen wir ISAR vor, ein Autorensystem für Augmented-Reality-Tabletops, das sich an Benutzer aus nicht-technischen Bereichen richtet. ISAR ermöglicht es nicht-technischen Anwendern, ihre eigenen interaktiven Tabletop-Anwendungen zu erstellen und mit dem Einsatz dieser Technologie in Bereichen wie Bildung, industrieller Ausbildung und medizinischer Rehabilitation zu experimentieren

A multi-camera approach to image-based rendering and 3-D/Multiview display of ancient chinese artifacts

published_or_final_versio

Intraoperative Endoscopic Augmented Reality in Third Ventriculostomy

In neurosurgery, as a result of the brain-shift, the preoperative patient models used as a intraoperative reference change. A meaningful use of the preoperative virtual models during the operation requires for a model update. The NEAR project, Neuroendoscopy towards Augmented Reality, describes a new camera calibration model for high distorted lenses and introduces the concept of active endoscopes endowed with with navigation, camera calibration, augmented reality and triangulation modules

Multi-Sensor Fusion for 3D Object Detection

Sensing and modelling of the surrounding environment is crucial for solving many of the problems in intelligent machines like self-driving cars, autonomous robots, and augmented reality displays. Performance, reliability and safety of the autonomous agents rely heavily on the way the environment is modelled. Two-dimensional models are inadequate to capture the three-dimensional nature of real-world scenes. Three-dimensional models are necessary to achieve the standards required by the autonomy stack for intelligent agents to work alongside humans. Data driven deep learning methodologies for three-dimensional scene modelling has evolved greatly in the past few years because of the availability of huge amounts of data from variety of sensors in the form of well-designed datasets. 3D object detection and localization are two of the key requirements for tasks such as obstacle avoidance, agent-to-agent interaction, and path planning. Most methodologies for object detection work on a single sensor data like camera or LiDAR. Camera sensors provide feature rich scene data and LiDAR provides us 3D geometrical information. Advanced object detection and localization can be achieved by leveraging the information from both camera and LiDAR sensors. In order to effectively quantify the uncertainty of each sensor channel, an appropriate fusion strategy is needed to fuse the independently encoded point clouds from LiDAR with the RGB images from standard vision cameras. In this work, we introduce a fusion strategy and develop a multimodal pipeline which utilizes existing state-of-the-art deep learning based data encoders to produce robust 3D object detection and localization in real-time. The performance of the proposed fusion model is evaluated on the popular KITTI 3D benchmark dataset

Augmented reality interaction and vision-based tracking

Master'sMASTER OF ENGINEERIN