Analysis domain model for shared virtual environments

The field of shared virtual environments, which also encompasses online games and social 3D environments, has a system landscape consisting of multiple solutions that share great functional overlap. However, there is little system interoperability between the different solutions. A shared virtual environment has an associated problem domain that is highly complex raising difficult challenges to the development process, starting with the architectural design of the underlying system. This paper has two main contributions. The first contribution is a broad domain analysis of shared virtual environments, which enables developers to have a better understanding of the whole rather than the part(s). The second contribution is a reference domain model for discussing and describing solutions - the Analysis Domain Model

Distributed shared memory for virtual environments

Bibliography: leaves 71-77.This work investigated making virtual environments easier to program, by designing a suitable distributed shared memory system. To be usable, the system must keep latency to a minimum, as virtual environments are very sensitive to it. The resulting design is push-based and non-consistent. Another requirement is that the system should be scaleable, over large distances and over large numbers of participants. The latter is hard to achieve with current network protocols, and a proposal was made for a more scaleable multicast addressing system than is used in the Internet protocol. Two sample virtual environments were developed to test the ease-of-use of the system. This showed that the basic concept is sound, but that more support is needed. The next step should be to extend the language and add compiler support, which will enhance ease-of-use and allow numerous optimisations. This can be improved further by providing system-supported containers

Collaborative Workspaces within Distributed Virtual Environments

In warfare, be it a training simulation or actual combat, a commander\u27s time is one of the most valuable and fleeting resources of a military unit. Thus, it is natural for a unit to have a plethora of personnel to analyze and filter information to the decision-maker. This dynamic exchange of ideas between analyst and commander is currently not available within the distributed interactive simulation (DIS) community. This lack of exchange limits the usefulness of the DIS experience to the commander and his troops. This thesis addresses the commander\u27s isolation problem through the integration of a collaborative workspace within AFIT\u27s Synthetic BattleBridge (SBB) as a technique to improve situational awareness. The SBB\u27s Collaborative Workspace enhances battlespace awareness through CSCW (computer supported cooperative work) enabling communication technologies. The SBB\u27s Collaborative Workspace allows the user to interact with other SBB users through the transmission and reception of public bulletins, private email, real-time chat sessions, shared viewpoints, shared video, and shared annotations to the virtual environment. Collaborative communication between SBB occurs through the use of standard and experimental DIS-compliant protocol data units. The SBB\u27s Collaborative Workspace gives the battlespace commander the widest range of communication options available within a DIS virtual environment today

A collaborative monocular visual simultaneous localization and mapping solution to generate a semi-dense 3D map.

The utilization and generation of indoor maps are critical in accurate indoor tracking. Simultaneous Localization and Mapping (SLAM) is one of the main techniques used for such map generation. In SLAM, an agent generates a map of an unknown environment while approximating its own location in it. The prevalence and afford-ability of cameras encourage the use of Monocular Visual SLAM, where a camera is the only sensing device for the SLAM process. In modern applications, multiple mobile agents may be involved in the generation of indoor maps, thus requiring a distributed computational framework. Each agent generates its own local map, which can then be combined with those of other agents into a map covering a larger area. In doing so, they cover a given environment faster than a single agent. Furthermore, they can interact with each other in the same environment, making this framework more practical, especially for collaborative applications such as augmented reality. One of the main challenges of collaborative SLAM is identifying overlapping maps, especially when the relative starting positions of the agents are unknown. We propose a system comprised of multiple monocular agents with unknown relative starting positions to generate a semi-dense global map of the environment

Middleware services for distributed virtual environments

PhD ThesisDistributed Virtual Environments (DVEs) are virtual environments which allow dispersed users to interact with each other and the virtual world through the underlying network. Scalability is a major challenge in building a successful DVE, which is directly affected by the volume of message exchange. Different techniques have been deployed to reduce the volume of message exchange in order to support large numbers of simultaneous participants in a DVE. Interest management is a popular technique for filtering unnecessary message exchange between users. The rationale behind interest management is to resolve the "interests" of users and decide whether messages should be exchanged between them. There are three basic interest management approaches: region-based, aura-based and hybrid approaches. However, if the time taken for an interest management approach to determine interests is greater than the duration of the interaction, it is not possible to guarantee interactions will occur correctly or at all. This is termed the Missed Interaction Problem, which all existing interest management approaches are susceptible to. This thesis provides a new aura-based interest management approach, termed Predictive Interest management (PIM), to alleviate the missed interaction problem. PIM uses an enlarged aura to detect potential aura-intersections and iii initiate message exchange. It utilises variable message exchange frequencies, proportional to the intersection degree of the objects' expanded auras, to restrict bandwidth usage. This thesis provides an experimental system, the PIM system, which couples predictive interest management with the de-centralised server communication model. It utilises the Common Object Request Broker Architecture (CORBA) middleware standard to provide an interoperable middleware for DVEs. Experimental results are provided to demonstrate that PIM provides a scalable interest management approach which alleviates the missed interaction problem

Middleware services for distributed virtual environments

PhD ThesisDistributed Virtual Environments (DVEs) are virtual environments which allow dispersed users to interact with each other and the virtual world through the underlying network. Scalability is a major challenge in building a successful DVE, which is directly affected by the volume of message exchange. Different techniques have been deployed to reduce the volume of message exchange in order to support large numbers of simultaneous participants in a DVE. Interest management is a popular technique for filtering unnecessary message exchange between users. The rationale behind interest management is to resolve the "interests" of users and decide whether messages should be exchanged between them. There are three basic interest management approaches: region-based, aura-based and hybrid approaches. However, if the time taken for an interest management approach to determine interests is greater than the duration of the interaction, it is not possible to guarantee interactions will occur correctly or at all. This is termed the Missed Interaction Problem, which all existing interest management approaches are susceptible to. This thesis provides a new aura-based interest management approach, termed Predictive Interest management (PIM), to alleviate the missed interaction problem. PIM uses an enlarged aura to detect potential aura-intersections and iii initiate message exchange. It utilises variable message exchange frequencies, proportional to the intersection degree of the objects' expanded auras, to restrict bandwidth usage. This thesis provides an experimental system, the PIM system, which couples predictive interest management with the de-centralised server communication model. It utilises the Common Object Request Broker Architecture (CORBA) middleware standard to provide an interoperable middleware for DVEs. Experimental results are provided to demonstrate that PIM provides a scalable interest management approach which alleviates the missed interaction problem

Consensus Based Networking of Distributed Virtual Environments

Distributed Virtual Environments (DVEs) are challenging to create as the goals of consistency and responsiveness become contradictory under increasing latency. DVEs have been considered as both distributed transactional databases and force-reflection systems. Both are good approaches, but they do have drawbacks. Transactional systems do not support Level 3 (L3) collaboration: manipulating the same degree-of-freedom at the same time. Force-reflection requires a client-server architecture and stabilisation techniques. With Consensus Based Networking (CBN), we suggest DVEs be considered as a distributed data-fusion problem. Many simulations run in parallel and exchange their states, with remote states integrated with continous authority. Over time the exchanges average out local differences, performing a distribued-average of a consistent, shared state. CBN aims to build simulations that are highly responsive, but consistent enough for use cases such as the piano-movers problem. CBN's support for heterogeneous nodes can transparently couple different input methods, avoid the requirement of determinism, and provide more options for personal control over the shared experience. Our work is early, however we demonstrate many successes, including L3 collaboration in room-scale VR, 1000's of interacting objects, complex configurations such as stacking, and transparent coupling of haptic devices. These have been shown before, but each with a different technique; CBN supports them all within a single, unified system

Modeling Human Group Behavior In Virtual Worlds

Virtual worlds and massively-multiplayer online games are rich sources of information about large-scale teams and groups, offering the tantalizing possibility of harvesting data about group formation, social networks, and network evolution. They provide new outlets for human social interaction that differ from both face-to-face interactions and non-physically-embodied social networking tools such as Facebook and Twitter. We aim to study group dynamics in these virtual worlds by collecting and analyzing public conversational patterns of users grouped in close physical proximity. To do this, we created a set of tools for monitoring, partitioning, and analyzing unstructured conversations between changing groups of participants in Second Life, a massively multi-player online user-constructed environment that allows users to construct and inhabit their own 3D world. Although there are some cues in the dialog, determining social interactions from unstructured chat data alone is a difficult problem, since these environments lack many of the cues that facilitate natural language processing in other conversational settings and different types of social media. Public chat data often features players who speak simultaneously, use jargon and emoticons, and only erratically adhere to conversational norms. Humans are adept social animals capable of identifying friendship groups from a combination of linguistic cues and social network patterns. But what is more important, the content of what people say or their history of social interactions? Moreover, is it possible to identify whether iii people are part of a group with changing membership merely from general network properties, such as measures of centrality and latent communities? These are the questions that we aim to answer in this thesis. The contributions of this thesis include: 1) a link prediction algorithm for identifying friendship relationships from unstructured chat data 2) a method for identifying social groups based on the results of community detection and topic analysis. The output of these two algorithms (links and group membership) are useful for studying a variety of research questions about human behavior in virtual worlds. To demonstrate this we have performed a longitudinal analysis of human groups in different regions of the Second Life virtual world. We believe that studies performed with our tools in virtual worlds will be a useful stepping stone toward creating a rich computational model of human group dynamics