Fireground location understanding by semantic linking of visual objects and building information models

This paper presents an outline for improved localization and situational awareness in fire emergency situations based on semantic technology and computer vision techniques. The novelty of our methodology lies in the semantic linking of video object recognition results from visual and thermal cameras with Building Information Models (BIM). The current limitations and possibilities of certain building information streams in the context of fire safety or fire incident management are addressed in this paper. Furthermore, our data management tools match higher-level semantic metadata descriptors of BIM and deep-learning based visual object recognition and classification networks. Based on these matches, estimations can be generated of camera, objects and event positions in the BIM model, transforming it from a static source of information into a rich, dynamic data provider. Previous work has already investigated the possibilities to link BIM and low-cost point sensors for fireground understanding, but these approaches did not take into account the benefits of video analysis and recent developments in semantics and feature learning research. Finally, the strengths of the proposed approach compared to the state-of-the-art is its (semi -)automatic workflow, generic and modular setup and multi-modal strategy, which allows to automatically create situational awareness, to improve localization and to facilitate the overall fire understanding

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

Learning Models for Following Natural Language Directions in Unknown Environments

Natural language offers an intuitive and flexible means for humans to communicate with the robots that we will increasingly work alongside in our homes and workplaces. Recent advancements have given rise to robots that are able to interpret natural language manipulation and navigation commands, but these methods require a prior map of the robot's environment. In this paper, we propose a novel learning framework that enables robots to successfully follow natural language route directions without any previous knowledge of the environment. The algorithm utilizes spatial and semantic information that the human conveys through the command to learn a distribution over the metric and semantic properties of spatially extended environments. Our method uses this distribution in place of the latent world model and interprets the natural language instruction as a distribution over the intended behavior. A novel belief space planner reasons directly over the map and behavior distributions to solve for a policy using imitation learning. We evaluate our framework on a voice-commandable wheelchair. The results demonstrate that by learning and performing inference over a latent environment model, the algorithm is able to successfully follow natural language route directions within novel, extended environments.Comment: ICRA 201

Elements of design for indoor visualisation

Indoor visualisation has received little attention. Research related to indoor environments have primarily focussed on the data structuring, localisation and navigation components (Zlatanova et al., 2013). Visualisation is an integral component in addressing the diverse array of indoor environments. In simple words, 'What is the most efficient way to visualise the surrounding indoor environment so that the user can concisely understand their surroundings as well as facilitating the process of navigation?' This dissertation proposes a holistic approach that consists of two components. The significance of this approach is that it provides a robust and adaptable method in providing a standard to which indoor visualisation can be referenced against. The first component is a theoretical framework focussing on indoor visualisation and it comprises of principles from several disciplines such as geovisualisation, human-perception theory, spatial cognition, dynamic and 3D environments as well as accommodating emotional processes resulting from human-computer interaction. The second component is based on the theoretical framework and adopts a practical approach towards indoor visualisation. It consists of a set of design properties that can be used for the design of effective indoor visualisations. The framework is referred to as the "Elements of Design" framework. Both these components aim to provide a set of principles and guidelines that can be used as best practices for the design of indoor visualisations. In order to practically demonstrate the holistic indoor visualisation approach, multiple indoor visualisation renderings were developed. The visualisation renderings were represented in a three-dimensional virtual environment from a first-person perspective. Each rendering used the design framework differently. Also, each rendering was graded using a parallel chart that compares how the different visual elements were used per the rendering. The main findings were that the techniques/ renderings that used the visual elements effectively (enhanced human-perception) resulted in better acquisition and construction of knowledge about the surrounding indoor environment

Service-oriented Context-aware Framework

Location- and context-aware services are emerging technologies in mobile and desktop environments, however, most of them are difficult to use and do not seem to be beneficial enough. Our research focuses on designing and creating a service-oriented framework that helps location- and context-aware, client-service type application development and use. Location information is combined with other contexts such as the users' history, preferences and disabilities. The framework also handles the spatial model of the environment (e.g. map of a room or a building) as a context. The framework is built on a semantic backend where the ontologies are represented using the OWL description language. The use of ontologies enables the framework to run inference tasks and to easily adapt to new context types. The framework contains a compatibility layer for positioning devices, which hides the technical differences of positioning technologies and enables the combination of location data of various sources

Emergency Evacuation Software Model For Simulation Of Physical Changes

Public space such as schools, cinemas, shopping malls, etc. must have an emergency evacuation system in place. Such places are also required to follow certain regulations and protocols for emergency evacuation to assure the safety of their occupants inside from any unpredictable incident. For nearly two decades, companies/organizations are using simulation models/software for evacuation planning. Researchers are working on these software models to improve the efficiency using latest algorithms. This thesis focuses on creating a base software model of evacuation systems for 3D indoor environments to simulate physical changes such as retractable chairs, movable walls etc., to evaluate their effectiveness before committing to those changes. This research tries to address various flaws and shortcomings of previous software. We are using tools like Unity 3D and Autodesk Maya to simulate suggested changes. It provides planners as well as researchers a new perspective to work on new recommended physical changes to design public venues