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

Map matching by using inertial sensors: literature review

This literature review aims to clarify what is known about map matching by using inertial sensors and what are the requirements for map matching, inertial sensors, placement and possible complementary position technology. The target is to develop a wearable location system that can position itself within a complex construction environment automatically with the aid of an accurate building model. The wearable location system should work on a tablet computer which is running an augmented reality (AR) solution and is capable of track and visualize 3D-CAD models in real environment. The wearable location system is needed to support the system in initialization of the accurate camera pose calculation and automatically finding the right location in the 3D-CAD model. One type of sensor which does seem applicable to people tracking is inertial measurement unit (IMU). The IMU sensors in aerospace applications, based on laser based gyroscopes, are big but provide a very accurate position estimation with a limited drift. Small and light units such as those based on Micro-Electro-Mechanical (MEMS) sensors are becoming very popular, but they have a significant bias and therefore suffer from large drifts and require method for calibration like map matching. The system requires very little fixed infrastructure, the monetary cost is proportional to the number of users, rather than to the coverage area as is the case for traditional absolute indoor location systems.Siirretty Doriast

Integrating Haptic Feedback into Mobile Location Based Services

Haptics is a feedback technology that takes advantage of the human sense of touch by applying forces, vibrations, and/or motions to a haptic-enabled device such as a mobile phone. Historically, human-computer interaction has been visual - text and images on the screen. Haptic feedback can be an important additional method especially in Mobile Location Based Services such as knowledge discovery, pedestrian navigation and notification systems. A knowledge discovery system called the Haptic GeoWand is a low interaction system that allows users to query geo-tagged data around them by using a point-and-scan technique with their mobile device. Haptic Pedestrian is a navigation system for walkers. Four prototypes have been developed classified according to the user’s guidance requirements, the user type (based on spatial skills), and overall system complexity. Haptic Transit is a notification system that provides spatial information to the users of public transport. In all these systems, haptic feedback is used to convey information about location, orientation, density and distance by use of the vibration alarm with varying frequencies and patterns to help understand the physical environment. Trials elicited positive responses from the users who see benefit in being provided with a “heads up” approach to mobile navigation. Results from a memory recall test show that the users of haptic feedback for navigation had better memory recall of the region traversed than the users of landmark images. Haptics integrated into a multi-modal navigation system provides more usable, less distracting but more effective interaction than conventional systems. Enhancements to the current work could include integration of contextual information, detailed large-scale user trials and the exploration of using haptics within confined indoor spaces

Robust localization with wearable sensors

Measuring physical movements of humans and understanding human behaviour is useful in a variety of areas and disciplines. Human inertial tracking is a method that can be leveraged for monitoring complex actions that emerge from interactions between human actors and their environment. An accurate estimation of motion trajectories can support new approaches to pedestrian navigation, emergency rescue, athlete management, and medicine. However, tracking with wearable inertial sensors has several problems that need to be overcome, such as the low accuracy of consumer-grade inertial measurement units (IMUs), the error accumulation problem in long-term tracking, and the artefacts generated by movements that are less common. This thesis focusses on measuring human movements with wearable head-mounted sensors to accurately estimate the physical location of a person over time. The research consisted of (i) providing an overview of the current state of research for inertial tracking with wearable sensors, (ii) investigating the performance of new tracking algorithms that combine sensor fusion and data-driven machine learning, (iii) eliminating the effect of random head motion during tracking, (iv) creating robust long-term tracking systems with a Bayesian neural network and sequential Monte Carlo method, and (v) verifying that the system can be applied with changing modes of behaviour, defined as natural transitions from walking to running and vice versa. This research introduces a new system for inertial tracking with head-mounted sensors (which can be placed in, e.g. helmets, caps, or glasses). This technology can be used for long-term positional tracking to explore complex behaviours

Neuroadaptive mobile geographic information displays: an emerging cartographic research frontier

Mobility, including navigation and wayfinding, is a basic human requirement for survival. For thousands of years maps have played a significant role for human mobility and survival. Increasing reliance on digital GNSS-enabled navigation assistance, however, is impacting human attentional resources and is limiting our innate cognitive spatial abilities. To mitigate human de-skilling, a neuroadaptive (mobile) cartographic research frontier is proposed and first steps towards creating well-designed mobile geographic information displays (mGIDs) that not only respond to navigators’ cognitive load and visuo-spatial attentional resources during navigation in real-time but are also able to scaffold spatial learning while still maintaining navigation efficiency. This in turn, will help humans to remain as independent from geoinformation technology, as desired. La mobilité, dont la navigation et l'orientation, est un besoin humain fondamental pour la survie. Pendant des milliers d'années, les cartes analogiques ont joué un rôle significatif pour la mobilité humaine et sa survie. Pourtant, la dépendance grandissante vis-à-vis de l'assistance à la navigation à l'aide de données numériques GNSS, impacte les ressources de l'attention humaine et limite nos capacités innées de cognition spatiale. Pour atténuer la perte de compétence humaine, un front de recherche sur la cartographie (mobile) neuroadaptative est proposé ainsi que des premières étapes pour la création d'écrans d'informations géographiques mobile (mGID) bien conçus, qui non seulement répondent à la charge cognitive et aux ressources de l'attention visio-spatiale des utilisateurs navigateurs pendant la navigation temps-réel mais aussi qui soient capables d'élaborer un apprentissage spatial tout en assurant l'efficacité de la navigation. Cela aidera les humains à rester aussi indépendant de la technologie de l'information géographique qu'ils le souhaitent

An Adaptive Human Activity-Aided Hand-Held Smartphone-Based Pedestrian Dead Reckoning Positioning System

Pedestrian dead reckoning (PDR), enabled by smartphones’ embedded inertial sensors, is widely applied as a type of indoor positioning system (IPS). However, traditional PDR faces two challenges to improve its accuracy: lack of robustness for different PDR-related human activities and positioning error accumulation over elapsed time. To cope with these issues, we propose a novel adaptive human activity-aided PDR (HAA-PDR) IPS that consists of two main parts, human activity recognition (HAR) and PDR optimization. (1) For HAR, eight different locomotion-related activities are divided into two classes: steady-heading activities (ascending/descending stairs, stationary, normal walking, stationary stepping, and lateral walking) and non-steady-heading activities (door opening and turning). A hierarchical combination of a support vector machine (SVM) and decision tree (DT) is used to recognize steady-heading activities. An autoencoder-based deep neural network (DNN) and a heading range-based method to recognize door opening and turning, respectively. The overall HAR accuracy is over 98.44%. (2) For optimization methods, a process automatically sets the parameters of the PDR differently for different activities to enhance step counting and step length estimation. Furthermore, a method of trajectory optimization mitigates PDR error accumulation utilizing the non-steady-heading activities. We divided the trajectory into small segments and reconstructed it after targeted optimization of each segment. Our method does not use any a priori knowledge of the building layout, plan, or map. Finally, the mean positioning error of our HAA-PDR in a multilevel building is 1.79 m, which is a significant improvement in accuracy compared with a baseline state-of-the-art PDR system

NavMarkAR: A Landmark-based Augmented Reality (AR) Wayfinding System for Enhancing Spatial Learning of Older Adults

Wayfinding in complex indoor environments is often challenging for older adults due to declines in navigational and spatial-cognition abilities. This paper introduces NavMarkAR, an augmented reality navigation system designed for smart-glasses to provide landmark-based guidance, aiming to enhance older adults' spatial navigation skills. This work addresses a significant gap in design research, with limited prior studies evaluating cognitive impacts of AR navigation systems. An initial usability test involved 6 participants, leading to prototype refinements, followed by a comprehensive study with 32 participants in a university setting. Results indicate improved wayfinding efficiency and cognitive map accuracy when using NavMarkAR. Future research will explore long-term cognitive skill retention with such navigational aids.Comment: 24 page

Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition

The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future

Haptic Transit: Tactile feedback to notify public transport users

To attract people to use public transport, efficient transit information systems providing accurate, real-time, easy-tounderstand information must be provided to users. In this paper we introduce HapticTransit, a tactile feedback based alert/notification model of a system, which provides spatial information to the public transport user. The model uses real-time bus location with other spatial information to provide feedback about the user as their journey is in progress. The system allows users make better use of „in-bus‟ time. It allows the user be involved with other activities and not be anxious about the arrival at their destination bus stop. Our survey shows a majority of users have missed a bus stop/station whilst undertaking a transit journey in an unfamiliar location. The information provided by our system can be of great advantage to certain user groups. The vibration alarm is used to provide tactile feedback. Visual feedback, in the form of colour coded buttons and textual description, is also provided. This model forms the basis for further research for developing information systems for public transport users with special needs – deaf, visually impaired and those with poor spatial abilities