Indoor Navigation Model Research Articles

Landmark-Based Indoor Navigation Model through Human Route Descriptions

Landmark-Based Indoor Navigation Model through Human Route Descriptions

Semantic enrichment of BIM with IndoorGML for quadruped robot navigation and automated 3D scanning

Planning scan routes with prior knowledge can improve scan data quality and completeness. This paper presents a BIM-enabled approach to optimize quadruped robot navigation for automated 3D scanning. The BIM data schema is enriched with IndoorGML, integrating building geometry with spatial data to establish an indoor navigation model describing multi-scale spatial topological networks. This navigation model, which includes an enhanced greedy algorithm, optimizes quadruped robot scanning positions and traversal sequences. The scan planning optimization outperforms existing heuristic algorithms in computational efficiency, coverage, and scan point count. The BIM-enabled approach is validated on ROS and in real-world conditions with a 3D LiDAR sensor integrated with a quadruped robot. The robotic scans achieve visible coverage of 70–90% of the structure, with a fluctuation of 0.006–0.021 mm compared to traditional laser scans. The findings demonstrate robotic scans as a viable way of obtaining complete and accurate point clouds, reducing human effort in traditional scanning.

Automation of escape route analysis for BIM-based building code checking

The use of BIM is becoming increasingly important in the AEC industry because the included dataset enables the automation of many processes. One emerging application of BIM is the automation of building code checking by building authorities. Building code checking is a time-consuming process, particularly for escape routes. Therefore, its automation promises considerable optimisation potential. This study presents a semi-automated escape route analysis for building code checking. Our developed concept consists of two automated steps, with a legally required manual step in between: (1) escape route generation, which provides possible routes from which designers can select a valid combination, and (2) escape route validation, which checks the selected combination of escape routes for validity. The basis of both automated steps is a novel indoor navigation model. We implemented the concept in Solibri Office and validated it with real-world models. The developed escape route analysis method shows potential to improve designers’ escape route planning and building authorities’ escape route checking.

Crowd-sourced AI based Indoor Localization using Support Vector Regression and Pedestrian Dead Reckoning

Aims and Background: Artificial intelligence (AI) is expanding in the market daily to assist humans in a variety of ways. However, as these models are expensive, there is still a gap in the availability of AI products to the common public with high component dependency. Objective and Methodology: To address the issue of additional component dependency on AI products, we propose a model that can use available Smartphone resources to perceive real-world huddles and assist ordinary people with their daily needs. The proposed AI model is to predict the user’s indoor position (Node) at the computer science and engineering block of CMR Institute of Technology (CMRIT) by using Smartphone sensors and wireless signals. We used SVR to predict the regular walk steps needed between two Nodes and Pedestrian Dead Reckoning (PDR) to predict the walk steps needed while the signal was lost in the indoor environment. Results: The Support vector regression (SVR) models make the locations to be available within the specified building boundaries for proper guidance. The PDR approach supports the user while signal loss between two Received Signal Strength Indicators (RSSI). The Pedestrian dead reckoning - Support Vector Regression (PD-SVR) results are showing 98% accuracy in NODE predictions with routing tables. The indoor positioning is 100% accurate with dynamic crowd-sourcing Node preparation. Conclusion: The results are compared with other indoor navigation models K-nearest neighbor (KNN) and DF-SVM are given 95% accurate NODE estimation with minimal need for network components.

The HDIN Dataset: A Real-World Indoor UAV Dataset with Multi-Task Labels for Visual-Based Navigation

Supervised learning for Unmanned Aerial Vehicle (UAVs) visual-based navigation raises the need for reliable datasets with multi-task labels (e.g., classification and regression labels). However, current public datasets have limitations: (a) Outdoor datasets have limited generalization capability when being used to train indoor navigation models; (b) The range of multi-task labels, especially for regression tasks, are in different units which require additional transformation. In this paper, we present a Hull Drone Indoor Navigation (HDIN) dataset to improve the generalization capability for indoor visual-based navigation. Data were collected from the onboard sensors of a UAV. The scaling factor labeling method with three label types has been proposed to overcome the data jitters during collection and unidentical units of regression labels simultaneously. An open-source Convolutional Neural Network (i.e., DroNet) was employed as a baseline algorithm to retrain the proposed HDIN dataset, and compared with DroNet’s pretrained results on its original dataset since we have a similar data format and structure to the DroNet dataset. The results show that the labels in our dataset are reliable and consistent with the image samples.

Indoor Positioning and Navigation Model Based on Semantic Grid

Traditional GPS positioning technology cannot be used in indoor space. With the development of the new positioning technology and the Internet of things, the indoor mobile object positioning and navigation model have been the focus of the relevant research institutions at home and abroad. Based on this, indoor positioning technology was studied starting from Wi-Fi, RFID, and iBeacon technology in this paper. However, the accuracy of indoor positioning and navigation needs to be further improved. This paper presents a semantic space model based on artificial intelligence technology, through semantic pattern matching, semantic concept extension, semantic reasoning and semantic mapping, and interior semantic localization is realized. The indoor semantic network and indoor grid navigation model are constructed, and the indoor semantic path is modeled from time, location, user, and congestion. At the same time, the improved Term Frequency-Inverse Document Frequency is combined with the Hidden Markov Model to improve the accuracy of matching the stay area with the most likely location to visit and improve the accuracy of semantic annotation. It was found that the research on the indoor positioning and navigation model based on the semantic grid can realize the uniform expression of the complex spatial semantics of the theme, geometry, connectivity, and distance, which can promote the development of indoor positioning and navigation.

An indoor navigation model and its network extraction

We propose a navigation model for indoor environments that combines a 3D geometric modeling of buildings with connection properties of spaces and semantic elements such as openings and installations. The model is an extension of the IndoorGML standard navigation module with a twofold benefit: the extension facilitated the data import from the international standard CityGML and introduced the semantics of various fixtures in indoor space of buildings making the navigation model more suitable for human needs. Several experiments have been conducted by extracting networks from CityGML data and performing a comparison with other network construction techniques. The second contribution of the paper is an algorithm for the automatic extraction of the navigation network. Such an algorithm is a hybrid solution between medial axis approaches and visibility graph approaches. Normally, medial axes approaches are a good representation of human navigation in narrow corridors, especially to avoid obstacles, but introduce distortions in open space. On the other hand, visibility approaches work better in open spaces. In our extraction technique, the resulting network takes advantages of both approaches and better mimics human beings’ navigation in indoor environments.

A Modified Methodology for Generating Indoor Navigation Models

Automatic methods for constructing navigation routes do not fully meet all requirements. The aim of this study was to modify the methodology for generating indoor navigation models based on the Medial Axis Transformation (MAT) algorithm. The simplified method for generating corridor axes relies on the Node-Relation Structure (NRS) methodology. The axis of the modeled structure (corridor) is then determined based on the points of the middle lines intersecting the structure (polygon). The proposed solution involves a modified approach to the segmentation of corridor space. Traditional approaches rely on algorithms for generating Triangulated Irregular Networks (TINs) by Delaunay triangulation or algorithms for generating Thiessen polygons known as Voronoi diagrams (VDs). In this study, both algorithms were used in the segmentation process. The edges of TINs intersected structures. Selected midpoints on TIN edges, which were located in the central part of the structure, were used to generate VDs. Corridor structures were segmented by polygon VDs. The identifiers or structure nodes were the midpoints on the TIN edges rather than the calculated centroids. The generated routes were not zigzag lines, and they approximated natural paths. The main advantage of the proposed solution is its simplicity, which can be attributed to the use of standard tools for processing spatial data in a geographic information system.

Indoor Semantic Modelling for Routing: The Two-Level Routing Approach for Indoor Navigation

Humans perform many activities indoors and they show a growing need for indoor navigation, especially in unfamiliar buildings such as airports, museums and hospitals. Complexity of such buildings poses many challenges for building managers and visitors. Indoor navigation services play an important role in supporting these indoor activities. Indoor navigation covers extensive topics such as: 1) indoor positioning and localization; 2) indoor space representation for navigation model generation; 3) indoor routing computation; 4) human wayfinding behaviours; and 5) indoor guidance (e.g., textual directories). So far, a large number of studies of pedestrian indoor navigation have presented diverse navigation models and routing algorithms/methods. However, the major challenge is rarely referred to: how to represent the complex indoor environment for pedestrians and conduct routing according to the different roles and sizes of users. Such complex buildings contain irregular shapes, large open spaces, complicated obstacles and different types of passages. A navigation model can be very complicated if the indoors are accurately represented. Although most research demonstrates feasible indoor navigation models and related routing methods in regular buildings, the focus is still on a general navigation model for pedestrians who are simplified as circles. In fact, pedestrians represent different sizes, motion abilities and preferences (e.g., described in user profiles), which should be reflected in navigation models and be considered for indoor routing (e.g., relevant Spaces of Interest and Points of Interest)...

Development of an Omnidirectional-Image-Based Data Model through Extending the IndoorGML Concept to an Indoor Patrol Service

Different indoor representation methods have been studied for their ability to provide indoor location-based services (LBS). Among them, omnidirectional imaging is one of the most typical and simple methods for representing an indoor space. However, a georeferenced omnidirectional image cannot be used for simple attribute searches, spatial queries, and spatial awareness analyses. To perform these functions, topological data are needed to define the features of and spatial relationships among spatial objects including indoor spaces as well as facilities like CCTV cameras considered in patrol service applications. Therefore, this study proposes an indoor space application data model for an indoor patrol service that can implement functions suited to linking indoor space data and service objects. In order to do this, the study presents a method for linking data between omnidirectional images representing indoor spaces and topological data on indoor spaces based on the concept of IndoorGML. Also, we conduct an experimental implementation of the integrated 3D indoor navigation model for patrol service using GIS data. Based on the results, we evaluate the benefits of using such a 3D data fusion method that integrates omnidirectional images with vector-based topological data models based on IndoorGML for providing indoor LBS in built environments.

Indoor Semantic Modelling for Routing: The Two-Level Routing Approach for Indoor Navigation

Indoor Semantic Modelling for Routing: The Two-Level Routing Approach for Indoor Navigation

AN AUTOMATED 3D INDOOR TOPOLOGICAL NAVIGATION NETWORK MODELLING

Abstract. Indoor navigation is important for various applications such as disaster management and safety analysis. In the last decade, indoor environment has been a focus of wide research; that includes developing techniques for acquiring indoor data (e.g. Terrestrial laser scanning), 3D indoor modelling and 3D indoor navigation models. In this paper, an automated 3D topological indoor network generated from inaccurate 3D building models is proposed. In a normal scenario, 3D indoor navigation network derivation needs accurate 3D models with no errors (e.g. gap, intersect) and two cells (e.g. rooms, corridors) should touch each other to build their connections. The presented 3D modeling of indoor navigation network is based on surveying control points and it is less dependent on the 3D geometrical building model. For reducing time and cost of indoor building data acquisition process, Trimble LaserAce 1000 as surveying instrument is used. The modelling results were validated against an accurate geometry of indoor building environment which was acquired using Trimble M3 total station.

Generating Navigation Models from Existing Building Data

Abstract. Research on indoor navigation models mainly focuses on geometric and logical models .The models are enriched with specific semantic information which supports localisation, navigation and guidance. Geometric models provide information about the structural (physical) distribution of spaces in a building, while logical models indicate relationships (connectivity and adjacency) between the spaces. In many cases geometric models contain virtual subdivisions to identify smaller spaces which are of interest for navigation (e.g. reception area) or make use of different semantics. The geometric models are used as basis to automatically derive logical models. However, there is seldom reported research on how to automatically realize such geometric models from existing building data (as floor plans) or indoor standards (CityGML LOD4 or IFC). In this paper, we present our experiments on automatic creation of logical models from floor plans and CityGML LOD4. For the creation we adopt the Indoor Spatial Navigation Model (INSM) which is specifically designed to support indoor navigation. The semantic concepts in INSM differ from daily used notations of indoor spaces such as rooms and corridors but they facilitate automatic creation of logical models.

Building Information Model for 3D Indoor Navigation in Emergency Response

In this paper, we discuss the possibility of using BIM into 3D indoor navigation in emergency response. We first analyze the functional requirements of 3D indoor emergency response, and then illustrate the workflow of transforming BIM into 3D, ontology-based, indoor navigation model, shortly as 3DOntoINM. Finally, we explain the algorithm and implication issues.

A Three-Dimensional Navigable Data Model to Support Emergency Response in Microspatial Built-Environments

Since the 11 September 2001 attacks in the United States and the 7 July 2005 London bombings, geospatial researchers have attempted to utilize GIScience technologies in response to disasters occurring in the microspace of multilevel structures (such as the interior of buildings) in urban areas. Such applications require 3D geographic information systems (GIS) functionalities to represent the three-dimensional structures of urban environments and to conduct 3D GIS-based spatial analyses. These requirements motivate this study to represent the complex internal structure of buildings at a three-dimensional subunit level so as to analyze human behavior in an emergency situation. This article discusses the development of a 3D Navigable Data Model (3D NDM) based on the 3D Geometric Network Data Model and representing pedestrian access within buildings or urban built-environments, which can be modeled as a network of walkway sections and connections. Additionally, this article discusses the design of a geospatial database created to manage the physical and environmental factors of disaster sites that are essential for emergency response. To improve planning and facilitate rescue operations in a decision support system, this article presents (1) a 3D geo-coding method to locate rescue personnel and disaster sites within the reference data (a network representation of a building), (2) a 3D map matching method to define the correlation between the location of bottlenecks or disaster sites and the nearest location on the 3D NDM, which represents the internal structure of the built-environment as a network representation, and (3) an indoor navigation model, based on the Dijkstra algorithm, to identify optimal routes within a multilevel structure by measuring relative pedestrian accessibility and to provide navigation guidance for rescue personnel. Lastly, this article presents the results of an experimental implementation of the 3D NDM using GIS data for a section of the University of North Carolina at Charlotte campus.