Indoor navigation map design based on the analysis of space characteristics

The application scope of geographical information science is gradually evolving from large-scale to small-scale environments (Afyouni et al., 2010). The space that we are dealing with is no longer limited to outdoor spaces but is extended to indoor spaces. Facing the indoor structure of complex buildings, the demand for indoor location services such as navigation and emergency evacuation services is increasing. Indoor navigation maps are an important tool for people to arrive at their destination in large public buildings. There are a lot of indoor navigation services to help mobile users but there are still some gaps between map design and the navigation process, such as how to model the path of the multi-dimensional structure of indoor environments, quantify the visibility condition of indoor areas, and compensate for the lack of semantic annotation of indoor corridors (e.g., there are typically no road signs as in the outdoor case). Most existing application studies focus on indoor maps that visualize the basic indoor spatial structures, while few take into account the navigation process in buildings. From the scientific perspective, there are a lot of aspects for designing indoor navigation maps (e.g., 2D/3D, visibility, and semantics). However, it is unclear which type of design is most effective for aiding pedestrians in indoor wayfinding. There has been some research on the design and representation of indoor maps. Nossum (2011) proposed a "Tubes" map representation method, which overlays the access information of different floors on the same plane, allowing users to understand the structure of each floor inside a building with the help of only one map. Li et al. (2013) studied indoor maps with multiple modes of representation on mobile terminals. They pointed out that both 2D and 3D maps significantly improved pointing and vertical navigation accuracy compared to the control condition with no map assistance, and argued that better visualization of the layered structure of the building could facilitate multi-level cognitive map development. The indoor space has special characteristics as the building space is divided by numerous walls and rooms, which limit the user's visual reach and hinder the overall perception of the space. In the process of indoor navigation, relevant studies have provided auxiliary guidance information for turns and specific decision points, adding guidance images, text, and symbols to convey information to users (De et al., 2019). It is also necessary to provide good navigational aids for areas with poor visibility. For example, Pang et al. (2021) generated an indoor visibility map based on a navigation network in corridor space. There are no names for the passages in an indoor space, but there are some landmarks, which are important elements for people to communicate route information, either verbally or graphically, and can assist pedestrians in making route decisions when they are at a fork along a path (May et al., 2003). In both outdoor and indoor environments, landmarks are generally selected considering the visual, semantic, and structural salience of the objects (Zhu et al., 2021; Zhou et al., 2022). Different from outdoor landforms, residential areas, water systems, vegetation, and other elements, indoor spaces are mainly artificially constructed entities. Indoor space elements refer to all the physical elements existing in the actual space, which describe the frame structure and local details of the indoor space. In map visualization, some elements are generally selected for mapping according to the map form, the specific purpose of the map, or the specific users (Ryder, 2015). According to the importance of the elements to the visualization of an indoor navigation map, the elements that are not salient enough for user attention and that have little or even interfering effects on reflecting the indoor navigation should be discarded

MONALISA 2.0 and the sea traffic management - a concept creating the need for new maritime information standards and software solutions

Postprint (published version

LandMarkAR: An application to study virtual route instructions and the design of 3D landmarks for indoor pedestrian navigation with a mixed reality head-mounted display

Mixed Reality (MR) interfaces on head-mounted displays (HMDs) have the potential to replace screen-based interfaces as the primary interface to the digital world. They potentially offer a more immersive and less distracting experience compared to mobile phones, allowing users to stay focused on their environment and main goals while accessing digital information. Due to their ability to gracefully embed virtual information in the environment, MR HMDs could potentially alleviate some of the issues plaguing users of mobile pedestrian navigation systems, such as distraction, diminished route recall, and reduced spatial knowledge acquisition. However, the complexity of MR technology presents significant challenges, particularly for researchers with limited programming knowledge. This thesis presents “LandMarkAR” to address those challenges. “LandMarkAR” is a HoloLens application that allows researchers to create augmented territories to study human navigation with MR interfaces, even if they have little programming knowledge. “LandMarkAR” was designed using different methods from human-centered design (HCD), such as design thinking and think-aloud testing, and was developed with Unity and the Mixed Reality Toolkit (MRTK). With “LandMarkAR”, researchers can place and manipulate 3D objects as holograms in real-time, facilitating indoor navigation experiments using 3D objects that serve as turn-by-turn instructions, highlights of physical landmarks, or other information researchers may come up with. Researchers with varying technical expertise will be able to use “LandMarkAR” for MR navigation studies. They can opt to utilize the easy-to-use User Interface (UI) on the HoloLens or add custom functionality to the application directly in Unity. “LandMarkAR” empowers researchers to explore the full potential of MR interfaces in human navigation and create meaningful insights for their studies

Use of Augmented Reality in Human Wayfinding: A Systematic Review

Augmented reality technology has emerged as a promising solution to assist with wayfinding difficulties, bridging the gap between obtaining navigational assistance and maintaining an awareness of one's real-world surroundings. This article presents a systematic review of research literature related to AR navigation technologies. An in-depth analysis of 65 salient studies was conducted, addressing four main research topics: 1) current state-of-the-art of AR navigational assistance technologies, 2) user experiences with these technologies, 3) the effect of AR on human wayfinding performance, and 4) impacts of AR on human navigational cognition. Notably, studies demonstrate that AR can decrease cognitive load and improve cognitive map development, in contrast to traditional guidance modalities. However, findings regarding wayfinding performance and user experience were mixed. Some studies suggest little impact of AR on improving outdoor navigational performance, and certain information modalities may be distracting and ineffective. This article discusses these nuances in detail, supporting the conclusion that AR holds great potential in enhancing wayfinding by providing enriched navigational cues, interactive experiences, and improved situational awareness.Comment: 52 page

Risk Assessment of Nautical Navigational Environment Based on Grey Fixed Weight Cluster

In order to set up a mathematical model suitable for nautical navigational environment risk evaluation and systematically master the navigational environment risk characteristics of the Qiongzhou Strait in a quantitative way, a risk assessment model with approach steps is set up based on the grey fixed weight cluster (GFWC). The evaluation index system is structured scientifically through both literature review and expert investigation. The relative weight of each index is designed to be obtained via fuzzy analytic hierarchy process (FAHP); Index membership degree of every grey class is proposed to be achieved by fuzzy statistics (FS) to avoid the difficulty of building whiten weight functions. By using the model, nautical navigational environment risk of the Qiongzhou Strait is determined at a “moderate” level according to the principle of maximum membership degree. The comprehensive risk evaluation of the Qiongzhou Strait nautical navigational environment can provide theoretical reference for implementing targeted risk control measures. It shows that the constructed GFWC risk assessment model as well as the presented steps are workable in case of incomplete information. The proposed strategy can excavate the collected experts’ knowledge mathematically, quantify the weight of each index and risk level, and finally lead to a comprehensive risk evaluation result. Besides, the adoptions of probability and statistic theory, fuzzy theory, aiming at solving the bottlenecks in case of uncertainty, will give the model a better adaptability and executability.</p

Passive Indoor Positioning System (PIPS) Using Near Field Communication (NFC) Technology

Travel can be an enjoyable experience but it can also be stressful when one is unable to get to the destination in timely manner. Satellite navigation systems (satnav) such as the ubiquitous Global Positioning System (GPS) provide an aid to locating unfamiliar places without hassle. However, the effectiveness of satnav stops at the doorstep of the building due to its requirement for line of sight with orbiting satellites. Within a large complex building, navigation typically relies on building signage, information from kiosks and getting assistance from information desks. The advancement of mobile devices and wireless technology offer an interesting proposition for the development of indoor positioning systems. In this paper, we propose a passive indoor positioning system to provide navigational aid and discuss findings from our pilot experiment using NFC technology

Indoor navigation for the visually impaired : enhancements through utilisation of the Internet of Things and deep learning

Wayfinding and navigation are essential aspects of independent living that heavily rely on the sense of vision. Walking in a complex building requires knowing exact location to find a suitable path to the desired destination, avoiding obstacles and monitoring orientation and movement along the route. People who do not have access to sight-dependent information, such as that provided by signage, maps and environmental cues, can encounter challenges in achieving these tasks independently. They can rely on assistance from others or maintain their independence by using assistive technologies and the resources provided by smart environments. Several solutions have adapted technological innovations to combat navigation in an indoor environment over the last few years. However, there remains a significant lack of a complete solution to aid the navigation requirements of visually impaired (VI) people. The use of a single technology cannot provide a solution to fulfil all the navigation difficulties faced. A hybrid solution using Internet of Things (IoT) devices and deep learning techniques to discern the patterns of an indoor environment may help VI people gain confidence to travel independently. This thesis aims to improve the independence and enhance the journey of VI people in an indoor setting with the proposed framework, using a smartphone. The thesis proposes a novel framework, Indoor-Nav, to provide a VI-friendly path to avoid obstacles and predict the user s position. The components include Ortho-PATH, Blue Dot for VI People (BVIP), and a deep learning-based indoor positioning model. The work establishes a novel collision-free pathfinding algorithm, Orth-PATH, to generate a VI-friendly path via sensing a grid-based indoor space. Further, to ensure correct movement, with the use of beacons and a smartphone, BVIP monitors the movements and relative position of the moving user. In dark areas without external devices, the research tests the feasibility of using sensory information from a smartphone with a pre-trained regression-based deep learning model to predict the user s absolute position. The work accomplishes a diverse range of simulations and experiments to confirm the performance and effectiveness of the proposed framework and its components. The results show that Indoor-Nav is the first type of pathfinding algorithm to provide a novel path to reflect the needs of VI people. The approach designs a path alongside walls, avoiding obstacles, and this research benchmarks the approach with other popular pathfinding algorithms. Further, this research develops a smartphone-based application to test the trajectories of a moving user in an indoor environment

DYNAMICS OF COLLABORATIVE NAVIGATION AND APPLYING DATA DRIVEN METHODS TO IMPROVE PEDESTRIAN NAVIGATION INSTRUCTIONS AT DECISION POINTS FOR PEOPLE OF VARYING SPATIAL APTITUDES

Cognitive Geography seeks to understand individual decision-making variations based on fundamental cognitive differences between people of varying spatial aptitudes. Understanding fundamental behavioral discrepancies among individuals is an important step to improve navigation algorithms and the overall travel experience. Contemporary navigation aids, although helpful in providing turn-by-turn directions, lack important capabilities to distinguish decision points for their features and importance. Existing systems lack the ability to generate landmark or decision point based instructions using real-time or crowd sourced data. Systems cannot customize personalized instructions for individuals based on inherent spatial ability, travel history, or situations. This dissertation presents a novel experimental setup to examine simultaneous wayfinding behavior for people of varying spatial abilities. This study reveals discrepancies in the information processing, landmark preference and spatial information communication among groups possessing differing abilities. Empirical data is used to validate computational salience techniques that endeavor to predict the difficulty of decision point use from the structure of the routes. Outlink score and outflux score, two meta-algorithms that derive secondary scores from existing metrics of network analysis, are explored. These two algorithms approximate human cognitive variation in navigation by analyzing neighboring and directional effect properties of decision point nodes within a routing network. The results are validated by a human wayfinding experiment, results show that these metrics generally improve the prediction of errors. In addition, a model of personalized weighting for users\u27 characteristics is derived from a SVMrank machine learning method. Such a system can effectively rank decision point difficulty based on user behavior and derive weighted models for navigators that reflect their individual tendencies. The weights reflect certain characteristics of groups. Such models can serve as personal travel profiles, and potentially be used to complement sense-of-direction surveys in classifying wayfinders. A prototype with augmented instructions for pedestrian navigation is created and tested, with particular focus on investigating how augmented instructions at particular decision points affect spatial learning. The results demonstrate that survey knowledge acquisition is improved for people with low spatial ability while decreased for people of high spatial ability. Finally, contributions are summarized, conclusions are provided, and future implications are discussed

Haptic Feedback to Assist Blind People in Indoor Environment Using Vibration Patterns

Feedback is one of the significant factors for the mental mapping of an environment. It is the communication of spatial information to blind people to perceive the surroundings. The assistive smartphone technologies deliver feedback for different activities using several feedback mediums, including voice, sonification and vibration. Researchers 0have proposed various solutions for conveying feedback messages to blind people using these mediums. Voice and sonification feedback are effective solutions to convey information. However, these solutions are not applicable in a noisy environment and may occupy the most important auditory sense. The privacy of a blind user can also be compromised with speech feedback. The vibration feedback could effectively be used as an alternative approach to these mediums. This paper proposes a real-time feedback system specifically designed for blind people to convey information to them based on vibration patterns. The proposed solution has been evaluated through an empirical study by collecting data from 24 blind people through a mixed-mode survey using a questionnaire. Results show the average recognition accuracy for 10 different vibration patterns are 90%, 82%, 75%, 87%, 65%, and 70%