Indoor Navigation Framework Research Articles

INFED: Enhancing fire evacuation dynamics through 3D congestion-aware indoor navigation framework

This paper introduces Indoor Navigation Framework for Fire Evacuation Dynamics (INFED), a novel indoor navigation framework that combines dynamic fire constraints and path congestion management. INFED considers the three-dimensional (3D) attributes of both the agents (speed, volume, location, count) and the environment (3D volume, congestion, corridor height, and corridor length) to estimate navigation routes that avoid fire-affected evacuation paths. It achieves this by integrating various proposed algorithms as modules: Environment Establisher, Fired/Safe Node Identifier, Pre-processor, Weighted Graph Generator, and Path Generator. The 3D features of the agent and environment are used to effectively estimate the capacity of the corridors in an indoor environment for the estimation of path congestion. The path congestion so computed is used during evacuation to identify the safest and congestion-free path. We discuss the performance of INFED by implementing it on various realistic scenarios in a commercial floor setup. We found that the incorporation of safety constraints results in longer evacuation routes, ranging from a 6% increase under mild fire and congestion conditions to a 40% increase under severe fire and congestion conditions. In the event of a worst-case scenario where fire-free paths are scarce, INFED utilizes congestion to reduce agent speed along the recommended evacuation route. This mechanism is activated when congestion surpasses a threshold of 0.3. The system can be used by stakeholders to test various evacuation hypotheses, which can lead to better preparedness and rescue operations, ultimately saving lives in the event of a fire.

First Responders Space Subdivision Framework for Indoor Navigation

Indoor navigation is crucial, particularly during indoor disasters such as fires. However, current spatial subdivision models struggle to adapt to the dynamic changes that occur in such situations, making it difficult to identify the appropriate navigation space, and thus reducing the accuracy and efficiency of indoor navigation. This study presents a new framework for indoor navigation that is specifically designed for first responders, with a focus on improving their response time and safety during rescue operations in buildings. The framework is an extension of previous research and incorporates the combustibility factor as a critical variable to consider during fire disasters, along with definitions of safe and unsafe areas for first responders. An algorithm was developed to accommodate the framework and was evaluated using Pyrosim and Pathfinder software. The framework calculates walking speed factors that affect the path and walking speed of first responders, enhancing their chances of successful evacuation. The framework captures dynamic changes, such as smoke levels, that may impact the navigation path and walking speed of first responders, which were not accounted for in previous studies. The experimental results demonstrate that the framework can identify suitable navigation paths and safe areas for first responders, leading to successful evacuation in as little as 148 to 239 seconds. The proposed framework represents a significant improvement over previous studies and has the potential to enhance the safety and effectiveness of first responders during emergency situations.

A new spatial database framework for pedestrian indoor navigation based on the OpenStreetMap tag information

AbstractIt is widely acknowledged that the tools for pedestrian navigation in indoor environments have been increasing while the internal spaces of buildings continue to grow in complexity. The majority of mobile applications for indoor navigation are mostly dependent on access to radio‐frequency identification (RFID) and WiFi, which are particularly limited and expensive in most of Sub‐Saharan Africa. In addition, data from Volunteered Geographic Information such as OpenStreetMap (OSM) lacked clear framework and specification for database design for indoor navigation. Accordingly, this study proposed and illustrated a new database framework for indoor navigation by taking advantage of the popular OSM tag information structure. The proposed framework characterized the indoor environment based on horizontal and vertical partitions, together with description of indoor features by using cardinal directions and qualitative descriptions. The framework was demonstrated by creating sample database records, and retrieval of step‐by‐step travel information for different internal configurations of the indoor environment. A key contribution of this framework is its simplistic and low‐cost nature, where user travel information is retrieved from the database with no need for communication signals from Global Positioning Systems, RFID, or WiFi, making it advantageous for low‐cost applications where access to these communication infrastructure are limited.

An Indoor Localization Method Based on the Combination of Indoor Map Information and Inertial Navigation with Cascade Filter

The specific objective of this study is to propose a low‐cost indoor navigation framework with nonbasic equipment by combining inertial sensors and indoor map messages. The proposed pedestrian navigation framework consists of a lower filter and an upper filter. In the lower filter which is designed based on the Kalman filter, the adaptive zero velocity detection algorithm is used to detect the zero velocity interval at different motion speeds, and then, zero velocity update is applied to rectify the inertial navigation solutions’ errors. In the upper filter which is designed based on the nonrecursive Bayesian filter, the map matching method with nonrecursive Bayesian filter is adopted to fuse the map prior information and the lower filter estimation results to correct the errors of navigation. The position estimation presented in this study achieves an average position error of 0.53 m compared to the ZUPT‐aided inertial navigation system (INS) method under different motion states. The proposed pedestrian navigation algorithm achieves an average position error of 0.54 m as compared to the ZUPT‐aided INS method among the different tested distances. The proposed framework simplifies the indoor positioning system under multiple motion speed conditions by ensuring the accuracy and stability property. The effectiveness and accuracy of the proposed framework are experimentally verified in various real‐world scenarios.

A Generic Approach toward Indoor Navigation and Pathfinding with Robust Marker Tracking

Indoor navigation and localization has gained a key attention of the researchers in the recent decades. Various technologies such as WiFi, Bluetooth, Ultra Wideband (UWB), and Radio-frequency identification (RFID) have been used for indoor navigation and localization. However, most of these existing methods often fail in providing a reasonable solution to the key challenges such as implementation cost, accuracy and extendibility. In this paper, we proposed a low-cost, and extendable framework for indoor navigation. We used simple markers printed on the paper, and placed on ceilings of the building. These markers are detected by a smartphone’s camera, and the audio and visual information associated with these markers are used as a user guidance. The system finds shortest path between any two arbitrary nodes for user navigation. In addition, it is extendable having the capability to cover new sections by installing new nodes at any place in the building. The system can be used for guidance of the blind people, tourists and new visitors in an indoor environment. The evaluation results reveal that the proposed system can guide users toward their destination in an efficient and accurate manner.

GoIn—An accurate 3D indoor navigation framework based on light landmarks

GoIn—An accurate 3D indoor navigation framework based on light landmarks

Building rating system: an instrument for building accessibility measurement for better indoor navigation by blind people

PurposeThe purpose of this paper is to create a building rating system (BRS) with its bottom-up design model that can be carried out manually and in the future automatically.Design/methodology/approachThe BRS is built on the basis of the structure of spatial representation framework for indoor navigation by people with visual impairment, which was validated with visually impaired people, and incorporated with building design standards and regulations from around the world. The BRS was afterwards validated by three groups of five experts in the related fields such as research and development, accessibility, and building and interior designs. Finally, the user evaluation was carried out by three focus groups of three experts in risk assessment to verify the usability of the system.FindingsThis paper provides the design and methodology of the BRS used for classifying the accessibility in buildings into four levels of classification for people with visual impairment navigating around the buildings. This system is evaluated with system usability scales (SUS), which is found to be in a “Good” level on average (72.2 SUS scores).Research limitations/implicationsSuccess criteria used in the space classification are mainly created for people with visual impairment at this stage; other disabilities requirements must be taken into account for the next stage of the development.Practical implicationsThe system can be carried out in the future automatically in the form of standalone software or plugins that can be integrated in buildings and interior design software to seek recommendations toward a creation of inclusive built environment.Originality/valueThis paper presents a design architecture of BRS with its details, description and success criteria used in the space classification.