This study investigates the feasibility of directly utilizing 3D indoor point clouds for real-time indoor navigation, particularly to enhance emergency response processes. Traditional indoor navigation research primarily focuses on creating navigation systems from pre-existing indoor models, resulting in a graph representation that simplifies spatial relationships, requires post-processing, and delivers results only afterwards, often overlooking real-time obstacles and complex layouts such as those in modern office floors. This research proposes an original approach by leveraging real-time generated 3D models using HoloLens 2 sensors, which combine RGB images and depth sensor output to create a comprehensive point cloud. The study explores path planning directly within these point clouds without the need for extensive preprocessing or segmentation, aiming to provide immediate navigation support with minimal delay. Utilizing the Rapidly Exploring Random Trees (RRT) algorithm, the research seeks to minimize preprocessing and swiftly visualize navigable paths, evaluating the system's performance in terms of processing time and path viability. This approach addresses the limitations of traditional graph-based methods and the challenges posed by outdated or unavailable indoor models, offering a promising solution for real-time emergency navigation assistance.@en

Emergency operations are a key example for the need of digital twins in the way it is complex, urgent and uncertain. First, the process is complex, as many organizations are involved. Second, it is urgent, as most damage is done in the first moments of an emergency. Third, it is uncertain, as situational conditions tend to change quickly. For outdoor operations, spatial information systems help in creating an overview of the situation, for example by displaying positions of first responder units involved with the incident. However, spatial data of indoor environments is scarce. Static information of the building, such as floor plans, are often outdated or non-existent. Dynamic operational data such as positions of first responders within the building are only available in a very limited way as well, and often without visual representation. To create situation awareness of indoor first responder operation environments, this paper successfully proposes a proof of concept with two objectives. First, the proof of concept will collect spatial environment data in the form of mapping and tracking data by using a Microsoft HoloLens. This means the geometry of the building will be collected, together with traversed routes within the building. Second, the data will be streamed and displayed to a remote first responder coordinator in real-time to create a common operational picture. This enables the coordinator to quickly build situation awareness of the operation environment, enabling the coordinator to improve the quality of decisions, thereby improving first responder performance. The proof of concept showed that situation awareness on all three levels increases with the real-time (live) availability (visualisations) of 3D indoor environments. This concept needs to be tested further on usability and performance.

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Because unknown interior layouts can have serious consequences in time-sensitive situations, crisis response teams request many potential solutions for visualizing indoor environments in crisis scenarios. This research uses a game engine to directly visualize point cloud data input of indoor environments for generating clear interaction between the environment and viewers, to aid decision-making in high-stress moments. The prospective final product is an integration of game-oriented visualization and cartography, hosted within Unreal Engine 4 (UE4), allowing users to navigate throughout an indoor environment, and customizing certain interaction features. The UE4 project consists of 4 modules: data preprocessing, render style, functional module, and user interface. Finally, this research uses a single-floor indoor point cloud dataset collected from a building in Rotterdam, the Netherlands for the implementation.@en

Indoor environments tend to be more complex and more populated when buildings are accessible to the public. The need for knowing where people are, how they can get somewhere or how to reach them in these buildings is thus equally increasing. In this research point clouds are used, obtained by dynamic laser scanning of a building, since we cannot rely on architectural drawings for maps and paths, which can be outdated. The presented method focuses on the creation of an indoor navigation graph, based on IndoorGML structure, in a fast and automated way, while retaining the type of walkable surface. In this paper the focus has been on door detection, because doors are essential elements in an indoor environment, seeing that they connect spaces and are a logical step in a route. This paper describes a way to detect doors using 3D Medial Axis Transform (MAT) combined with the intelligence stored in the path of a mobile laser scanner, showing good first results. Additionally different spaces (e.g. rooms and corridors) in the building are identified and slopes and stairs in walkable spaces are detected. This results in a navigation graph which can be stored in an IndoorGML structure.@en

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.

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Generation of indoor networks for navigation will normally be done out of standard floor plans that are only 2D and is more often manual than automatic. These floor plans are drawn at a specific time and do not correspond to the reality, moreover some of those buildings were built already differently than designed. Then in due course the building will change both externally and internally. Also objects like furniture will be moved around in the building. If these changes are not updated in the map of the building, it becomes out of date and cannot be used for the creation of indoor navigable models anymore. To enable correct indoor navigation, we will need to have the current data of the indoor environment. This article concentrates on providing a new approach to generate up to date floor plans by using a mobile (and hand held) laser scanner in the fastest way. This device creates a point cloud and the corresponding trajectory at the same time. Because the mobile laser scanner device is operated by a walking human, the trajectory contains information about the surface the person is walking on. In this article, a method is explained for the detection of walkable spaces based on the analysis of the point cloud and its corresponding trajectory provided by the mobile laser scanner. Three steps will be used: voxelization, trajectory analysis and the identification of floor regions. Dynamic objects, doors, and furniture objects are also used to identify the surfaces which are available for navigation purposes. Three types of surfaces are considered: horizontal, slopes, and stairs.@en

When the fire brigade arrives at a burning building, it is of vital importance that people who are still inside can quickly be found. Smart buildings should be able to expose this location data to the fire brigade working in a smart city. In this paper the feasibility is researched of using ultrasonic sound sensors for human presence detection in smoke-filled spaces. This type of sensor could assist the fire brigade when evacuating a large building by directing them to the places where their help is most needed. The advantage of ultrasonic sound over other sensors or cameras is that its signal is able to pierce through smoke, does not require badges or other wearable devices and introduces little privacy and security risks. In addition, ultrasonic sensors are very inexpensive making it possible to equip every room of a building with an ultrasonic presence detector. In this research both a preliminary ultrasound measuring device and signal processing algorithm have been designed. Testing results show that the walking movement of a person in an indoor area can be detected with the combination of the sensor and the algorithms. In addition, tests of the signal strength in smoke have shown that ultrasound is capable of “looking through” the smoke. The algorithm based on a particle filter allows for more information to be extracted from the relatively simple sensor signal by detecting human walking movement specifically and opens up the way for an ultrasound based indoor positioning system that can be used in emergency situations.@en

Since GPS tends to fail for indoor positioning purposes, alternative methods like indoor positioning systems (IPS) based on Bluetooth low energy (BLE) are developing rapidly. Generally, IPS are deployed in environments covered with obstacles such as furniture, walls, people and electronics influencing the signal propagation. The major factor influencing the system performance and to acquire optimal positioning results is the geometry of the beacons. The geometry of the beacons is limited to the available infrastructure that can be deployed (number of beacons, basestations and tags), which leads to the following challenge: Given a limited number of beacons, where should they be placed in a specified indoor environment, such that the geometry contributes to optimal positioning results? This paper aims to propose a statistical model that is able to select the optimal configuration that satisfies the user requirements in terms of precision. The model requires the definition of a chosen 3D space (in our case 7 × 10 × 6 meter), number of beacons, possible user tag locations and a performance threshold (e.g. required precision). For any given set of beacon and receiver locations, the precision, internal-and external reliability can be determined on forehand. As validation, the modeled precision has been compared with observed precision results. The measurements have been performed with an IPS of BlooLoc at a chosen set of user tag locations for a given geometric configuration. Eventually, the model is able to select the optimal geometric configuration out of millions of possible configurations based on a performance threshold (e.g. required precision).@en

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.

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At the most fundamental level, smart buildings deliver useful building services that make occupants productive. Smart asset management in hostipals starts with knowing the whereabouts of medical equipment. This paper investigates the subject of indoor localization of medical equipment in hospitals by defining functional spaces. In order to localize the assets indoors, a localization method is developed that takes into account several factors such as geometrical influences, characteristics of the Quuppa positioning system and obstructions in the indoor environment. For matching the position data to a real world location, several location types are developed by subdividing the floor plan into location clusters. The research has shown that a high-performance level can be achieved for locations that are within the high-resolution range of the receiver. The performance at the smallest subspaces can only be achieved when having a dense distribution of receivers. Test cases that were defined for specific situations in the test-area show successful localization in these subspaces for the majority of the test data.@en