Cartographical Aspects in the Design of Indoor Navigation Systems

Abstract. Recent years have witnessed the rapid development of navigation systems. They gradually become the default way of navigating in our daily life. Despite benefiting from their convenience, we may be negatively influenced by them (Parush et al. 2007). Current navigation systems usually draw too much of users' attention to the mobile devices, which leads to users’ lacking interaction with the environment. It is often the case that people easily forget travelled route and get lost without navigation systems (Reilly et al. 2008). Empirical study shows that by dividing attention, navigation systems impair users’ spatial memory, and thus impact their navigation skills (Gardony et al. 2013). Since a higher engagement with the environment can improve users’ spatial knowledge acquisition without decreasing efficiency (Brügger et al. 2017), navigation systems should incorporate important environmental features.As objects of visual, cognitive or structural importance (Sorrows & Hirtle 1999), landmarks have been extensively studied, and considered as an important environmental feature to be incorporated into navigation systems (Nuhn & Timpf 2018, Richter 2017, Duckham et al. 2010, Raubal & Winter 2002). They serve as references to provide users with more natural and pleasant navigating experience (Richter & Winter 2014). However, most of landmark-related studies are in outdoor environment. Although researchers have attempted to classify and assess indoor landmarks (Ohm 2014, Viaene 2014), it is still very difficult to determine suitable ones. As a subset of semantic indoor landmarks, existing navigational aids (e.g. signs and floor plans) are designed to assist navigation. Among them, signs are most preferred by users (Wang et al. 2017). Being the signs that give necessary information to route users to possible destinations, directional signs contribute most to wayfinding (Gibson 2009). With route information explicitly presented by them, they are relevant environmental features to be incorporated into indoor navigation systems.In this study, we therefore propose to incorporate directional signs into indoor navigation systems, in order to guide users’ attention back to the environment, to facilitate their spatial learning and improve their spatial knowledge acquisition.We incorporated directional signs into a proof-of-concept indoor navigation system. First, we integrated directional signs and their semantics into a graph-based model. A directional sign was modelled as a node. Each destination on the sign was linked with the sign by an edge. In order to differentiate it from the normal edge, it was marked as semantic edge. Thus, the semantics of the sign was modelled directly into the graph, instead of being stored as attributes in a previous version of our study (Wang & Gartner 2018). Then, a top-K shortest path algorithm was employed to find candidate routes (Yen 1971). In the end, the route with the largest coverage of semantic edges was suggested to the user. The users would then be explicitly guided to follow the directional signs in the environment.In the next step, we will evaluate the efficiency and effectiveness of the proposed navigation system by an empirical study with human subjects. Within the experiment, we will also investigate on whether this kind of guidance can succeed in drawing users’ attention back to the environment and support their spatial knowledge acquisition.

Innovative technologies have been incorporated into intelligent transportation systems (ITS) to improve sustainability, safety, and efficiency, hence revolutionising traditional transportation. The combination of three-dimensional (3D) indoor building mapping and navigation is a groundbreaking development in the field of ITS. A novel methodology, the “Three-Dimensional Routing Information Framework “(3D RIF), is designed to improve indoor navigation systems in the field of ITS. By leveraging the Quantum Geographic Information System (QGIS), this framework can produce three-dimensional routing data and incorporate sophisticated routing algorithms to handle the complexities associated with indoor navigation. The paper provides a detailed examination of how the framework can be implemented in transport systems in urban environments, with a specific focus on optimising indoor navigation for various applications, including emergency services, tourism, and logistics. The framework includes real-time updates and point-of-interest information, thereby enhancing the overall indoor navigation experience. The 3D RIF’s framework boosts the efficiency and effectiveness of intelligent transportation services by optimising the utilisation of internal resources. The research outcomes are emphasised, demonstrating a mean enhancement of around 25.51% in travel. The measurable enhancement highlighted in this statement emphasises the beneficial influence of ITS on the efficiency of travel, hence underscoring the significance of the ongoing progress in this field.

GPS-based navigation systems have been developed to support the walking of visually impaired people. However, their use in station premises and malls presented difficulties due to radio wave interference. In such places, an indoor navigation system (IDNS) was indispensable. Recently, an IDNS using geomagnetism that did not require the installation of new infrastructure attracted our attention. It incorporated technology to detect self-position by association with a geomagnetic fingerprint (GMFP) database, which included the geomagnetic vector of the current position, stored in advance on the cloud. However, to realize navigation over an entire floor, a detailed geomagnetic map of the floor was needed, taking a lot of time and labor to measure the geomagnetic field. In this research, we proposed a novel method that limited the area of the GMFP to places above braille blocks and linked the geometric maps to braille block maps. The main features of our system were as follows: (1) Reduction of labor and time required to make the GMFP; (2) Realization of a real-time navigation system with little error in estimated position; (3) A high-resolution geomagnetic map database could be easily made by walking with a nearly fixed stride length without any specific equipment. To confirm the principal operation of our system, we made a prototype system. We then evaluated the performance of our system under various experimental conditions. Finally, we demonstrated a simple navigation system. As a result, we were able to confirm that our system could estimate self-position in real-time with little error and provide simple navigation for visually impaired people.

Because of its high time-domain resolution, ultra-wideband (UWB) technology is capable of precise range measurement and has been applied in many accurate indoor positioning systems. This paper presents a practical indoor positioning and navigation system using the UWB modules we developed based on Decawave UWB chipset. In order to enhance the performance of the system in NLOS scenarios, a neural network approach is integrated. The feasibility of the combined approach is analysed and its accuracy is evaluated. The experiment results indicate that the neural network can maintain a stable accuracy no matter whether it is in an NLOS scenario and it is capable to identify the locations on two trajectories which are next to each other with very high confidence.

People generally find themselves lost while visiting a new indoor location because they are unaware of the building’s architecture, especially when it is a large one or a shopping mall. The Global Position System (GPS) does not work properly in the indoor environment because of the satellite signal attenuation. In this paper, to assist people in finding their path, a Li-Fi (Light-Fidelity) based Indoor Positioning System (IPS) is proposed. A framework is developed based on a Li-Fi LED lamp transmitter and a dongle receiver connected to an Android smartphone to decode the received sequence. The pathfinding graph-based algorithm is proceeded in a REST architecture by a Web service consulting the graph-path database both installed on a Raspberry pi 4. The proposed solution is a low cost and does not require any additional infrastructure. It is easy to implement in most indoor environments like hospitals, buildings, and campuses. A short survey of techniques and algorithms for indoor positioning and navigation with the help of smartphones is also presented.

Car navigation systems have made place everywhere around the globe. Outdoor navigation systems can be widely located in almost every geographical location that has access to the internet. However, indoor navigation system is very important for the pedestrians to find out their ways in complicated indoor areas such as shopping malls, subways, universities, exhibitions, underground areas, and tunnels etc. The recent rapid expansion of Wi-Fi zones and the increase of Wi-Fi equipped smartphones have enabled transformation of this idea to the reality. This work presents university indoor navigation system designed to help students, staff, and visitors to find their desired location. The work shows promising results when using distance measuring algorithm, the Trilateration, and the ITU-R P.1238 model together using the Network Simulator 2 “NS2” for the indoor navigation.

Named data networking (NDN) is a new and promising Internet architecture, which aims to replace the current transmission control protocol/Internet protocol (TCP/IP)-based Internet. The NDN internet architecture has introduced several benefits in numerous applications. An NDN-based indoor positioning and navigation system can further optimize existing localization technologies with reduced the server load and faster response time. In this study, an NDN-based approach for the existing Wi-Fi fingerprinting-based indoor positioning and navigation was investigated. Among the many features of NDN, the network-level caching can reduce the computational load and response time of the localization and navigation server. The theoretical analysis of the runtime complexity shows that the NDN's network-level caching performance is better than those of the conventional algorithms. In this paper, naming methods for different services and a server-side algorithm for handling the NDN requests are proposed. The real-world implantation and testing results show a better overall performance than that of TCP/IP. This network-level optimization for indoor positioning and navigation opens new opportunities because it can be combined with other application-level optimization techniques for more efficient indoor positioning and navigation in the future.

This chapter presents a cost-effective system for indoor localization and navigation that does not require the use of satellite positioning or data communication networks. The system, implemented as a smartphone app, relies on QR codes that are pre-generated and attached to the walls inside the building. By utilizing the information from these codes and the smartphone's inertial motion unit (IMU) sensors processed by the Pedestrian Dead-Reckoning (PDR) algorithm, the user's current position can be determined. The Dijkstra navigation algorithm is then used to guide the user to the desired destination. The smartphone app can also be used as a healthcare logistics service in mass-casualty incidents for collecting and reporting georeferenced triage decisions to the cloud.

Accurate and fast indoor Location-Based Services (LBS) is very important for daily life and emergency response. Indoor map is the basis of indoor LBS. The model construction and data organization of indoor map are the key scientific problems that urgently need to be solved in the current indoor LBS application. In recent years, hybrid models have been used widely in the research of indoor map, because they can balance the limitations of single models. However, the current studies about hybrid model pay more attention to the model accuracy and modeling algorithm, while ignoring its relationship between positioning and navigation and its practicality in mobile indoor LBS applications. This paper addresses a new indoor map model, named Building Information Modeling based Positioning and Navigation (BIMPN), which is based on the entity model and the network model. The highlight of BIMPN is that it proposes a concept of Step Node (SN) to assist indoor positioning and navigation function. We developed the Mobile Indoor Positioning and Navigation System (MIPNS) to verify the practicability of BIMPN. Results indicate that the BIMPN can effectively organize the characteristics of indoor spaces and the building features, and assist indoor positioning and navigation. The BIMPN proposed in this paper can be used for the construction of indoor maps and it is suitable for mobile indoor positioning and navigation systems.

This work designs and develops indoor and outdoor navigation architecture for accurate positioning in small areas. It combines sophisticated technologies, including GPS, Google Maps API, and spatial and descriptive data, to create a precise and user-friendly navigation solution. In addition, maintaining an updated database and integrating interactive features facilitates the adoption of future advanced technologies such as Bluetooth Low Energy (BLE) and Wi-Fi networks for improved accuracy in indoor navigation. This architecture is applied at the University of Mosul to guide students and visitors aboard the large university with many campus facilities. The application allows users to find specific colleges and facilities, look for destinations, and find the shortest way to reach their desired location. The testing results show that the application is efficient regarding location accuracy and response speed, with a 95% user satisfaction rate.

User location and tracking information are increasingly used for contact tracing and social community detection. In-door positioning and indoor navigation systems are reaching good performances in several realistic scenarios. After an evaluation exclusively done through simulations, nowadays, these systems are trying to reach robust performances and good accuracy in heterogeneous environments. Problems are manifold as each environment presents a structure that strongly affects inertial sensors and radio signal propagation. Generally, systems showing the best performances rely on an extended knowledge of the indoor map. Moreover, they implement a model for pedestrian dynamics in terms of e.g step length, stride and the behaviour of the target users. Experimental results obtained during realistic indoor competitions, clearly show that performances drop when such systems are used in unseen scenarios in which an external user test the proposed solution. In fact, many parameters that are generally calibrated and set to maximize the performances might not work as expected. In this paper, we highlight which best practices should be applied for model calibration in smartphone-based indoor positioning systems. We describe a reference system based on a particle filter, and we show the most relevant parameters and the main factors that are generally in common with all similar systems in the literature. We also present the Run-Once tool for reaching optimal parameters, highlighting those best practices that should be applied to indoor positioning systems to maximize their performances and improve their robustness.

Since the well-established navigation systems such as GPS are ineffective in indoor environments, research into developing novel indoor positioning technologies has emerged in recent years. While several technologies are being investigated, a practical and reliable indoor positioning system is yet to emerge. Indoor positioning using light signals holds a great potential to provide a reliable solution to the indoor positioning problem. This paper presents a comprehensive review of the available literature on light-based indoor positioning systems. The paper establishes a classification based on the design of various light-based indoor positioning (LIP) systems, and discusses their key components including hardware, software and algorithms. The limitations and challenges of the LIP systems are discussed in details, and current published solutions are presented. The review also provides a comparison between LIP systems and other optical positioning systems, and identifies the associated research gaps. Finally, based on the identified gaps, the future research directions in both LIP systems and other optical positioning systems are presented.

Industry 4.0, often known as the fourth industrial revolution, is the fourth version of the industrial revolution. Through the emergent and disruptive intelligence and information technologies, Industry 4.0 is increasingly being advocated as the way to improve productivity, foster economic growth, and ensure the sustainability of industrial enterprises. The workforce in the Industry 4.0 needs easy indoor navigation. This is inevitable in the case of a large industry. Contribution to this we have developed a smartphone-based system to help the workforce not get lost or waste time by providing them with a total overview of the factory on a digital map. The developed system provides workforce-friendly indoor navigation. In this work, we have developed an android application with a digital map interface for indoor navigation based on Bluetooth low energy (BLE) nodes. The system uses the trilateration technique for position estimation using the received signal strength indicator (RSSI) from BLE nodes. The shortest path from source to destination is calculated using Dijkstra's algorithm. The developed system can be used for the workforce in Industry 4.0 such as logistics, smart buildings, smart healthcare, and even smart urban governance by the various parties involved. The system was developed for the employees in the Muthoot Institute of Science and Technology.

Indoor navigation will become an important application on a smartphone for Location-Based Service (LBS).An indoor navigation system should work under normal circumstances and during emergencies, such as fires, during a building power shut down, alarm, etc.The LBS should be able to help users find the best exit route to the outside of the building under all circumstances and with high reliability.In this research, we develop an indoor ontology model for indoor navigation.This ontology model defines the indoor environment attributes such as location nodes, and connection points.The location nodes with the location information allow navigation in the indoor environment.Connection points are able to separate the map zones and the building floors into a "Map sheet."This ontology approach allows the LBS works in both normal circumstances and emergencies.This model provides a reliable indoor navigation system for LBS.

The researchers and developers of mobile AR platforms need to use a common platform for developing experiences regardless of the surroundings of the user. In order to expand the use of AR both indoor and outdoor with and without computer vision techniques, the breadth of options available for positioning users and points of interest needs to expand. Separately, the experts in indoor positioning and navigation are generally not as familiar with AR use scenarios as they are with other domains. Together, positioning and navigation experts, and mobile AR experts, will discuss: — What are the indoor positioning and navigation systems best suited for mobile AR? — What studies are underway or need to be conducted in order to advance this field?