Excavation work takes place almost continually in most cities around the Western hemisphere. Many cities are already full of infrastructures, buried networks, and street furniture, so excavation work is not without any thread to the operator and surrounding environment. Small construction sites, for example, are often constrained by operating infrastructure on surface level and underground. Although different agencies and network owners have information about the location of the objects that put excavation work at risk, this information is not centralized. Different organizations manage location information of buried cables, unexploded ordnance, and pollution, for example. This significantly complicates the early-stage planning and last minute risk assessment processes because professionals need to manually collect, assess, and integrate data about subsurface objects into a comprehensive risk assessment. To smoothen this process, ExcaSafeZone project, therefore, develops a system that collects location data, defines expert-based rules for safety risk assessment, and that synthesizes this into an open source prototype that visualized safety risks on a heat map.

An indoor logical network qualitatively represents abstract relationships between indoor spaces, and it can be used for path computation. In this paper, we concentrate on the logical network that does not have notions for metrics. Instead, it relies on the semantics and properties of indoor spaces. A navigation path can be computed by deriving parameters from these semantics and minimizing them in routing algorithms. Although previous studies have adopted semantic approaches to build logical networks, routing methods are seldom elaborated. The main issue with such networks is to derive criteria for path computation using the semantics of spaces. Here, we present a routing mechanism that is based on a dedicated space classification and a set of routing criteria. The space classification reflects characteristics of spaces that are important for navigation, such as horizontal and vertical directions, doors and windows, etc. Six routing criteria are introduced, and they involve: (1) the spaces with the preferred semantics; and/or (2) their centrality in the logical network. Each criterion is encoded as the weights to the nodes or edges of the logical network by considering the semantics of spaces. Logical paths are derived by a traditional shortest-path algorithm that minimizes these weights. Depending on the building’s interior configuration, one criterion may result in several logical paths. Therefore, we introduce a priority ordering of criteria to support path selection and decrease the possible number of logical paths. We provide a proof-of-concept implementation for several buildings to demonstrate the usability of such a routing. The main benefit of this routing method is that it does not need geometric information to compute a path. The logical network can be created using verbal descriptions only, and this routing method can be applied to indoor spaces derived from any building subdivision.

3D indoor navigation in multi-story buildings and under changing environments is still difficult to perform. 3D models of buildings are commonly not available or outdated. 3D point clouds turned out to be a very practical way to capture 3D interior spaces and provide a notion of an empty space. Therefore, pathfinding in point clouds is rapidly emerging. However, processing of raw point clouds can be very expensive, as these are semantically poor and unstructured data. In this article we present an innovative octree-based approach for processing of 3D indoor point clouds for the purpose of multi-story pathfinding. We semantically identify the construction elements, which are of importance for the indoor navigation of humans (i.e., floors, walls, stairs, and obstacles), and use these to delineate the available navigable space. To illustrate the usability of this approach, we applied it to real-world data sets and computed paths considering user constraints. The structuring of the point cloud into an octree approximation improves the point cloud processing and provides a structure for the empty space of the point cloud. It is also helpful to compute paths sufficiently accurate in their consideration of the spatial complexity. The entire process is automatic and able to deal with a large number of multi-story indoor environments.

As we realize that we spend most of our time in increasingly complex indoor environments, applications to assist indoor activities (e.g. guidance) have gained a lot of attention in the recent years. The advances in ubiquitous computing made possible the development of several spatial models intending to support context-aware and fine-grained indoor navigation systems. However, the available models often rely on simplified representations (e.g. 2D plans) and ignore the indoor features (e.g. furniture), thereby missing to reflect the complexity of the indoor environment. In this paper, we introduce the Flexible Space Subdivision framework (FSS) that allows to automatically identify the spaces that can be used for indoor navigation purpose. We propose a classification of indoor objects based on their ability to autonomously change location and we define a spatial subdivision of the indoor environment based on the classified objects and their functions. The framework can consider any 3D indoor configuration, the static and dynamic activities it hosts and it enables the possibility to consider all types of locomotion (e.g. walking, flying, etc.). It relies on input 3D models with geometric, semantic and topological information and identifies a set of subspaces with dedicated properties. We assess the framework against criteria defined in previous researches and we provide an example.

Precise and comprehensive knowledge about 3D urban space is required for simulation and analysis in the fields of urban and environmental planning, city administration and disaster management. In order to facilitate these applications, geo-information about functional, semantic, and topographic aspects of urban features, their mutual dependencies and relations is needed. Substantial work has been done in the modelling and representation of above-ground features in the context of 3D city modelling. However, the belowground part of the real world, of which utility networks form a big part, is often neglected. Existing data models for utility networks are generally very domain-specific and, therefore, not suitable either. This paper describes a 3D data modelling approach for integrated management of below-ground utility networks and related above-ground city objects. This approach consists of manipulating first the structure of existing utility data in the commonly used Feature Manipulation Engine ETL software in order to make the data compliant to the CityGML Utility Network ADE data model. Subsequently, workspaces are created that take care of storing the CityGML data into the free and open-source 3D City Database, which has been extended in order to manage utility network data, too. Moreover, the research shows the suitability of the extended 3DCityDB to perform graph-based topological operations by means of the PostgreSQL pgRouting extension. Lastly, the results are visualized in typical GIS applications, e.g. QGIS and ArcGIS.

Visibility is a common measure to describe the spatial properties of an environment related to the spatial behaviour. Isovists represent the space that can be seen from one observation point, and they are used to analyse the existence of obstacles affecting or blocking intervisibility in an area. Although point clouds depict the as-built reality in a very detailed and accurate way, literature addressing the analysis of visibility in 3D, and more specifically the usage of point clouds to visibility analysis, is rather limited. In this paper, a methodology to evaluate visibility from point clouds in indoor environments is proposed, resulting in the creation of 3D isovists. Point cloud is firstly discretized in a voxel-based structure and voxels are labelled into ‘exterior’, ‘occupied’, ‘visible’ and ‘occluded’ based on an occupancy followed by a visibility analysis performed from a ray-tracing algorithm. 3D Isovists are created from the boundary of visible voxels from an observer position and considering as input parameters the visual angle, maximum line of sight, and eye gaze direction.

Updated and detailed indoor models are being increasingly demanded for various applications such as emergency management or navigational assistance. The consolidation of new portable and mobile acquisition systems has led to a higher availability of 3D point cloud data from indoors. In this work, we explore the combined use of point clouds and trajectories from SLAM-based laser scanner to automate the reconstruction of building indoors. The methodology starts by door detection, since doors represent transitions from one indoor space to other, which constitutes an initial approach about the global configuration of the point cloud into building rooms. For this purpose, the trajectory is used to create a vertical point cloud profile in which doors are detected as local minimum of vertical distances. As point cloud and trajectory are related by time stamp, this feature is used to subdivide the point cloud into subspaces according to the location of the doors. The correspondence between subspaces and building rooms is not unambiguous. One subspace always corresponds to one room, but one room is not necessarily depicted by just one subspace, for example, in case of a room containing several doors and in which the acquisition is performed in a discontinue way. The labelling problem is formulated as combinatorial approach solved as a minimum energy optimization. Once the point cloud is subdivided into building rooms, envelop (conformed by walls, ceilings and floors) is reconstructed for each space. The connectivity between spaces is included by adding the previously detected doors to the reconstructed model. The methodology is tested in a real case study.

This paper introduces and compares two types of GML-based data standards for indoor location-based services, i.e., iIndoorGML and iIndoorLocationGML. By elaborating the advantages of the both standards and their data models, we conclude that the two data standards are complementary to each other. A jointed data model is presented to show the integration of the two standards. iIndoorGML can supply subdivision of building for data of iIndoorLocationGML, and the semantics of locations defined in iIndoorLocationGML can be added to iIndoorGML. By proposing two use cases, we take the initiative in attempting to combine the use of the two standards. The first case is to collect details from files of the two standards for an indoor path; the second one is to generate verbal directions for indoor guidance from files of the two standards. Some future work is given for further development, such as automatic integration of separate data from both standards.

In this paper, we study path planning for first responders in the presence of uncertain moving obstacles. To support the path planning, in our research we use hazard simulation to provide the predicted information of moving obstacles. A major problem in using hazard simulation is that the simulation results may involve uncertainty due to model errors or noise in the real measurements. To address this problem, we provide an approach to handle the uncertainty in the information of moving obstacles, and apply it to the case of toxic plumes. Our contribution consists of two parts: 1) a spatial data model that supports the representation of uncertain obstacles from hazard simulations and their influence on the road network and 2) a modified A* algorithm that can deal with the uncertainty and generate fast and safe routes passing though the obstacles. The experimental results show the routing capability of our approach and its potential for the application to real disasters.

At present, 87 % of people's activities are in indoor environment; indoor navigation has become a research issue. As the building structures for people's daily life are more and more complex, many obstacles influence humans' moving. Therefore it is essential to provide an accurate and efficient indoor path planning. Nowadays there are many challenges and problems in indoor navigation. Most existing path planning approaches are based on 2D plans, pay more attention to the geometric configuration of indoor space, often ignore rich semantic information of building components, and mostly consider simple indoor layout without taking into account the furniture. Addressing the above shortcomings, this paper uses BIM (IFC) as the input data and concentrates on indoor navigation considering obstacles in the multi-floor buildings. After geometric and semantic information are extracted, 2D and 3D space subdivision methods are adopted to build the indoor navigation network and to realize a path planning that avoids obstacles. The 3D space subdivision is based on triangular prism. The two approaches are verified by the experiments.

Automatic generation of indoor navigable models is mostly based on 2D floor plans. However, in many cases the floor plans are out of date. Buildings are not always built according to their blue prints, interiors might change after a few years because of modified walls and doors, and furniture may be repositioned to the user's preferences. Therefore, new approaches for the quick recording of indoor environments should be investigated. This paper concentrates on laser scanning with a Mobile Laser Scanner (MLS) device. The MLS device stores a point cloud and its trajectory. If the MLS device is operated by a human, the trajectory contains information which can be used to distinguish different surfaces. In this paper a method is presented for the identification of walkable surfaces based on the analysis of the point cloud and the trajectory of the MLS scanner. This method consists of several steps. First, the point cloud is voxelized. Second, the trajectory is analysing and projecting to acquire seed voxels. Third, these seed voxels are generated into floor regions by the use of a region growing process. By identifying dynamic objects, doors and furniture, these floor regions can be modified so that each region represents a specific navigable space inside a building as a free navigable voxel space. By combining the point cloud and its corresponding trajectory, the walkable space can be identified for any type of building even if the interior is scanned during business hours.