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"Smart" devices, such as contemporary smartphones and PDAs (Personal Digital Assistance), play a significant role in our daily live, be it for navigation or location-based services (LBSs). In this paper, the use of Ultra-Wide Band (UWB) and Wireless Fidelity (Wi-Fi) based on RTT (Round-Trip Time) measurements is investigated for pedestrian user localization. For this purpose, several scenarios are designed either using real observation or simulated data. In addition, the localization of user groups within a neighborhood based on collaborative navigation (CP) is investigated and analyzed. An analysis of the performance of these techniques for ranging the positioning estimation using different fusion algorithms is assessed. The methodology applied for CP leverages the hybrid nature of the range measurements obtained by UWB and Wi-Fi RTT systems. The proposed approach stands out due to its originality in two main aspects: (1) it focuses on developing and evaluating suitable models for correcting range errors in RF-based TWR (Two-Way Ranging) technologies, and (2) it emphasizes the development of a robust CP engine for groups of pedestrians. The results obtained demonstrate that a performance improvement with respect to position trueness for UWB and Wi-Fi RTT cases of the order of 74% and 54%, respectively, is achieved due to the integration of these techniques. The proposed localization algorithm based on a P2I/P2P (Peer-to-Infrastructure/Peer-to-Peer) configuration provides a potential improvement in position trueness up to 10% for continuous anchor availability, i.e., UWB known nodes or Wi-Fi access points (APs). Its full potential is evident for short-duration events of complete anchor loss (P2P-only), where an improvement of up to 53% in position trueness is achieved. Overall, the performance metrics estimated based on the extensive evaluation campaigns demonstrate the effectiveness of the proposed methodologies. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel switched approach to perform smartphone-based pedestrian navigation tasks even in scenarios where GNSS signals are unavailable. Specifically, when GNSS signals are available, the proposed approach estimates both the position and the average bias affecting the measurements from the accelerometers. This average bias is then utilized to denoise the accelerometer data when GNSS signals are unavailable. We test the effectiveness of denoising the acceleration measurements through the estimated average bias by a synthetic example. The effectiveness of the proposed approach is then validated through a real experiment which is conducted along a pre-planned 150 m path. [ABSTRACT FROM AUTHOR]

Map-matching is a core functionality of pedestrian navigation applications. The localization errors of the global positioning systems (GPSs) in smartphones are one of the most critical factors that limit the large-scale deployment of pedestrian navigation applications, especially in dense urban areas where multiple road segments exist within the range of GPS errors, which can be increased by tall buildings neighboring each other. In this paper, we address two issues of practical importance for map-matching based on the Hidden Markov Model (HMM) in pedestrian navigation systems: large localization error in the initial phase of map-matching and HMM breaks in open field traversals. A heuristic method to determine the probability of initial states of the HMM based on a small number of GPS data received during the short warm-up period is proposed to improve the accuracy of initial map-matching. A simple but highly practical method based on a heuristic evaluation of near-future locations is proposed to prevent the malfunction of the Viterbi algorithm within the area of open fields. The results of field experiments indicate that the enhanced HMM constructed via the proposed methods achieves significantly higher map-matching accuracy compared to that of state of the art. [ABSTRACT FROM AUTHOR]

In the pedestrian navigation system, researchers have reduced measurement errors and improved system navigation performance by fusing measurements from multiple low-cost inertial measurement unit (IMU) arrays. Unfortunately, the current data fusion methods for inertial sensor arrays ignore the system error compensation of individual IMUs and the correction of position information in the zero-velocity interval. Therefore, these methods cannot effectively reduce errors and improve accuracy. An error compensation method for pedestrian navigation systems based on a low-cost array of IMUs is proposed in this paper. The calibration method for multiple location-free IMUs is improved by using a sliding variance detector to segment the angular velocity magnitude into stationary and motion intervals, and each IMU is calibrated independently. Compensation is then applied to the velocity residuals in the zero-velocity interval after zero-velocity update (ZUPT). The experimental results show a significant improvement in the average noise performance of the calibrated IMU array, with a 3.01-fold increase in static noise performance. In the closed-loop walking experiment, the average horizontal position error of a single calibrated IMU is reduced by 27.52% compared to the uncalibrated IMU, while the calibrated IMU array shows a 2.98-fold reduction in average horizontal position error compared to a single calibrated IMU. After compensating for residual velocity, the average horizontal position error of a single IMU is reduced by 0.73 m, while that of the IMU array is reduced by 64.52%. [ABSTRACT FROM AUTHOR]

Pedestrian navigation methods based on inertial sensors are commonly used to solve navigation and positioning problems when satellite signals are unavailable. To address the issue of heading angle errors accumulating over time in pedestrian navigation systems that rely solely on the Zero Velocity Update (ZUPT) algorithm, it is feasible to use the pedestrian's motion constraints to constrain the errors. Firstly, a human step length model is built using human kinematic data collected by the motion capture system. Secondly, we propose the bipedal constraint algorithm based on the established human step length model. Real field experiments demonstrate that, by introducing the bipedal constraint algorithm, the mean biped radial errors of the experiments are reduced by 68.16% and 50.61%, respectively. The experimental results show that the proposed algorithm effectively reduces the radial error of the navigation results and improves the accuracy of the navigation. [ABSTRACT FROM AUTHOR]

The zero-velocity update (ZUPT) algorithm is a pivotal advancement in pedestrian navigation accuracy, utilizing foot-mounted inertial sensors. Its key issue hinges on accurately identifying periods of zero-velocity during human movement. This paper introduces an innovative adaptive sliding window technique, leveraging the Fourier Transform to precisely isolate the pedestrian's gait frequency from spectral data. Building on this, the algorithm adaptively adjusts the zero-velocity detection threshold in accordance with the identified gait frequency. This adaptation significantly refines the accuracy in detecting zero-velocity intervals. Experimental evaluations reveal that this method outperforms traditional fixed-threshold approaches by enhancing precision and minimizing false positives. Experiments on single-step estimation show the adaptability of the algorithm to motion states such as slow, fast, and running. Additionally, the paper demonstrates pedestrian trajectory localization experiments under a variety of walking conditions. These tests confirm that the proposed method substantially improves the performance of the ZUPT algorithm, highlighting its potential for pedestrian navigation systems. [ABSTRACT FROM AUTHOR]

The dataset proposed is a collection of pedestrian navigation data sequences combining visual and spatial information. The pedestrian navigation sequences are situations encountered by a pedestrian walking in an urban outdoor environment, such as moving on the sidewalk, navigating through a crowd, or crossing a street when the pedestrian light traffic is green. The acquired data are timestamped provided RGB-D images and are associated with GPS, and inertial data (acceleration, rotation). These recordings were acquired by separate processes, avoiding delays during their capture to guarantee a synchronisation between the moment of acquisition by the sensor and the moment of recording on the system. The acquisition was made in the city of Dijon, France, including narrow streets, wide avenues, and parks.Annotations of the RGB-D are also provided by bounding boxes indicating the position of relevant static or dynamic objects present in a pedestrian area, such as a tree, bench, or person. This pedestrian navigation dataset aims to support the development of smart mobile systems to assist visually impaired people in their daily movements in an outdoor environment. In this context, the visual data and localisation sequences we provide can be used to elaborate the appropriate visual processing methods to extract relevant information about the obstacles and their current positions on the path. Alongside the dataset, a visual-to-auditory substitution method has been employed to convert each image sequence into corresponding stereophonic sound files, allowing for comparison and evaluation. Synthetic sequences associated with the same information set are also provided based on the recordings of a displacement within the 3D model of a real place in Dijon.

In this paper, we propose a novel switched approach to perform smartphone-based pedestrian navigation tasks even in scenarios where GNSS signals are unavailable. Specifically, when GNSS signals are available, the proposed approach estimates both the position and the average bias affecting the measurements from the accelerometers. This average bias is then utilized to denoise the accelerometer data when GNSS signals are unavailable. We test the effectiveness of denoising the acceleration measurements through the estimated average bias by a synthetic example. The effectiveness of the proposed approach is then validated through a real experiment which is conducted along a pre-planned 150 m path.

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Micro-Electro-Mechanical System (MEMS) inertial sensors, characterized by their small size, low cost, and low power consumption, are commonly used in foot-mounted wearable pedestrian autonomous positioning systems. However, they also have drawbacks such as heading drift and poor repeatability. To address these issues, this paper proposes an improved pedestrian autonomous 3D positioning algorithm based on dual-foot motion characteristic constraints. Two sets of small-sized Inertial Measurement Units (IMU) are worn on the left and right feet of pedestrians to form an autonomous positioning system, each integrated with low-cost, low-power micro-inertial sensor chips. On the one hand, an improved adaptive zero-velocity detection algorithm is employed to enhance discrimination accuracy under different step-speed conditions. On the other hand, considering the dual-foot gait characteristics and the height difference feature during stair ascent and descent, horizontal position update algorithms based on dual-foot motion trajectory constraints and height update algorithms based on dual-foot height differences are, respectively, designed. These algorithms aim to re-correct the pedestrian position information updated at zero velocity in both horizontal and vertical directions. The experimental results indicate that in a laboratory environment, the 3D positioning error is reduced by 93.9% compared to unconstrained conditions. Simultaneously, the proposed approach enhances the accuracy, continuity, and repeatability of the foot-mounted IMU positioning system without the need for additional power consumption. [ABSTRACT FROM AUTHOR]

Despite the existence of various sensor errors and users' complex movements, smartphone sensor-based positioning systems have significant potential for indoor location-based services (LBS). In this paper, a method for indoor positioning in three smartphone carrying modes (i.e., Texting, Calling, and Swinging) using accelerometer, gravity, magnetometer, gyroscope, and pressure sensors data is proposed. The data of the gyroscope, gravity, and magnetometer sensors are integrated to estimate the heading using the Kalman filter (KF) based on the quaternion of the orientation of the phone. To evaluate the heading estimation method, five different methods including the magnetic (combined with the accelerometer and gravity sensors data), Madgwick, Extended Kalman filter (EKF), and proposed KF are implemented independently for different carrying modes. Gravity and magnetometer information is used to update the gyroscope state model. Finally, the 2D position is estimated based on the length and heading information of the steps. To develop the system for 3D positioning applications, the pressure sensor of the smartphone is employed to measure the changes in the pressure related to the vertical displacement of the user and the changes at the floor level. However, this method as a relative positioning method suffers from the cumulative error problem, which prevents its independent implementation. The heading and step length errors cause deviation from the main path of movement and cut off the walls. A new map matching method is introduced by examining the user's position and the way the path crosses through the wall to prevent the wall-crossing problem. The results show that the proposed algorithm significantly improves the accuracy of position estimation. The mean absolute reference position error for the three smartphone carrying modes (i.e., texting, calling, and swinging) was reduced from 3.43 to 0.59, from 3.56 to 0.67, and from 4.83 to 0.77, respectively. [ABSTRACT FROM AUTHOR]

Walking is a means of health promotion, which is one of the main features of smart cities. A smart city’s built environment can help people choose a healthy walking route instead of the shortest one. Our study investigated which environmental factors pedestrians who select healthy routes prefer and favored environmental factors in pedestrian navigation mobile applications. Survey data were collected from 164 residents in Daegu, South Korea, from October 12 to October 25, 2022. t and chi-square tests were used to compare perceptual differences between the healthy route and the shortest route preference groups. The results indicate that 56.7% of respondents preferred a healthy walking route over the shortest route. Pedestrians who chose the healthy route preferred to have less noise and more greenery along their commute and feel safer from traffic accidents and crimes than those who chose the shortest route. Moreover, people who favored healthy routes also considered the following environmental factors in pedestrian navigation mobile applications: (a) greenery and waterfront areas, (b) low traffic volume, and (c) safety from traffic accidents and crimes. The results suggest that urban planning and design policies support healthier and more active walking in smart cities.

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Unlike car navigation, where almost all vehicles can traverse every route, one route might not be optimal or even suitable for all pedestrians. Route geometry information, including tortuosity, twists and turns along roads, junctions, and road slopes, among others, matters a great deal for specific types of pedestrians, particularly those with limited mobility, such as wheelchair users and older adults. Offering practical routing services to these users requires that pedestrian navigation systems provide further information on route geometry. Therefore, this article proposes a novel method for extracting and analyzing the geometry properties of the shortest pedestrian paths, with a focus on open geospatial data across four aspects: (a) similarity, (b) route curviness, (c) road turns and intersections, and (d) road gradients. Deriving from the Hausdorff distance, a metric called the "dissimilarity ratio" was developed, allowing us to determine whether pairs of routes show any tendencies to be similar to each other. Using the "sinuosity index", a segment-based technique quantified the route curviness based on the number and degree of the road turns along the route. Moreover, relying upon open elevation data, the road gradients were extracted to identify routes offering smoother motion and better accessibility. Lastly, the road turns and intersections were investigated as pedestrian convenience and safety indicators. A local government area of Greater Sydney in Australia was chosen as the case study. The analysis was implemented on OpenStreetMap (OSM) shortest pedestrian paths against Google Maps as a benchmark for real-world commercial applications. The similarity analysis indicated that over 90% of OSM routes were identical or roughly similar to Google Maps. In addition, while Spearman's rank correlation showed a direct relationship between route curviness and route length, r S (758) = 0.92, p < 0.001, OSM, on average, witnessed more tortuous routes and, consequently, shorter straight roads between turns. However, OSM routes could be more suitable for pedestrians when the frequency of intersections and road slopes are at the center of attention. Finally, the devised metrics in this study, including the dissimilarity ratio and sinuosity index, showed their practicability in translating raw values into meaningful indicators. [ABSTRACT FROM AUTHOR]

Smartphone map-based pedestrian navigation is known to have a negative effect on the long-term acquisition of spatial knowledge and memorisation of landmarks. Landmark-based navigation has been proposed as an approach that can overcome such limitations. In this work, we investigate how different interaction technologies, namely smartphones and augmented reality (AR) glasses, can affect the acquisition of spatial knowledge when used to support landmark-based pedestrian navigation. We conducted a study involving 20 participants, using smartphones or augmented reality glasses for pedestrian navigation. We studied the effects of these systems on landmark memorisation and spatial knowledge acquisition over a period of time. Our results show statistically significant differences in spatial knowledge acquisition between the two technologies, with the augmented reality glasses enabling better memorisation of landmarks and paths. [ABSTRACT FROM AUTHOR]

Navigation has become an indispensable technology, especially when exploring unfamiliar environments. However, the existing shortest route-based navigation systems only focus on route effectiveness, which may deprive users of the opportunity to explore new areas. Hence, there is a need for a new navigation service that enriches our walking activities and maximizes their benefits in our daily lives by stimulating our natural tendency to explore. In this paper, we revisit navigation services from two orthogonal technological perspectives: 1) navigation using non-shortest routes; and 2) navigation across multiple days. Based on these two perspectives, we explain the relationship among related studies and develop a new heuristic search-based method to create diverse multiday routes. This could help construct a new navigation service to increase the graph roaming entropy values on those generated routes.

A pedestrian navigation system consisting of two foot-mounted strapdown inertial navigation systems (SINS)s is considered. The zero velocity conditions of the foot in the stance phase of a step and a limited distance between the feet are used for SINS corrections. The aim of the work is to study some consistency properties of the extended Kalman filter. It is shown that this consistency depends on a form of the error equations. [ABSTRACT FROM AUTHOR]

Many studies have focused on the smartphone-based global navigation satellite system (GNSS) for its portability. However, complex urban environments, such as urban canyons and tunnels, can easily interfere with GNSS signal qualities. Current smartphone-based positioning technologies using the GNSS signal still pose great challenges. Since the last satellite of the BeiDou navigation system (BDS) was successfully launched on 23 June 2020, it is possible to use a low-cost Android device to realize the localization based on the BDS signals worldwide. This research focuses on smartphone-based outdoor pedestrian navigation utilizing the GPS/BDS multi-constellation system. To improve the localization accuracy, we proposed the Weighted Factor Graph Optimization localization model (W-FGO). In this paper, firstly, we evaluate the signal qualities of the BDS via the data collected by the static experiment. Then, we structure the cost function based on the pseudo-range and the time series data for the traditional Factor Graph Optimization (FGO). Finally, we design the weight model based on the signal quality of each satellite and the time fading factor to further improve the localization accuracy of the conventional FGO method. An Android smartphone is utilized to collect the GNSS data for the evaluation and the localization. The experiment results demonstrate the superior performance of the proposed method. [ABSTRACT FROM AUTHOR]

In the pedestrian navigation system, researchers have reduced measurement errors and improved system navigation performance by fusing measurements from multiple low-cost inertial measurement unit (IMU) arrays. Unfortunately, the current data fusion methods for inertial sensor arrays ignore the system error compensation of individual IMUs and the correction of position information in the zero-velocity interval. Therefore, these methods cannot effectively reduce errors and improve accuracy. An error compensation method for pedestrian navigation systems based on a low-cost array of IMUs is proposed in this paper. The calibration method for multiple location-free IMUs is improved by using a sliding variance detector to segment the angular velocity magnitude into stationary and motion intervals, and each IMU is calibrated independently. Compensation is then applied to the velocity residuals in the zero-velocity interval after zero-velocity update (ZUPT). The experimental results show a significant improvement in the average noise performance of the calibrated IMU array, with a 3.01-fold increase in static noise performance. In the closed-loop walking experiment, the average horizontal position error of a single calibrated IMU is reduced by 27.52% compared to the uncalibrated IMU, while the calibrated IMU array shows a 2.98-fold reduction in average horizontal position error compared to a single calibrated IMU. After compensating for residual velocity, the average horizontal position error of a single IMU is reduced by 0.73 m, while that of the IMU array is reduced by 64.52%.

Pedestrian navigation methods based on inertial sensors are commonly used to solve navigation and positioning problems when satellite signals are unavailable. To address the issue of heading angle errors accumulating over time in pedestrian navigation systems that rely solely on the Zero Velocity Update (ZUPT) algorithm, it is feasible to use the pedestrian’s motion constraints to constrain the errors. Firstly, a human step length model is built using human kinematic data collected by the motion capture system. Secondly, we propose the bipedal constraint algorithm based on the established human step length model. Real field experiments demonstrate that, by introducing the bipedal constraint algorithm, the mean biped radial errors of the experiments are reduced by 68.16% and 50.61%, respectively. The experimental results show that the proposed algorithm effectively reduces the radial error of the navigation results and improves the accuracy of the navigation.

The zero-velocity update (ZUPT) algorithm is a pivotal advancement in pedestrian navigation accuracy, utilizing foot-mounted inertial sensors. Its key issue hinges on accurately identifying periods of zero-velocity during human movement. This paper introduces an innovative adaptive sliding window technique, leveraging the Fourier Transform to precisely isolate the pedestrian’s gait frequency from spectral data. Building on this, the algorithm adaptively adjusts the zero-velocity detection threshold in accordance with the identified gait frequency. This adaptation significantly refines the accuracy in detecting zero-velocity intervals. Experimental evaluations reveal that this method outperforms traditional fixed-threshold approaches by enhancing precision and minimizing false positives. Experiments on single-step estimation show the adaptability of the algorithm to motion states such as slow, fast, and running. Additionally, the paper demonstrates pedestrian trajectory localization experiments under a variety of walking conditions. These tests confirm that the proposed method substantially improves the performance of the ZUPT algorithm, highlighting its potential for pedestrian navigation systems.

Our study aims to devise a new method for route planning that caters to the distinct needs of elderly pedestrians. We established a quantitative model to capture the relationship between the characteristics of elderly users and their route preferences. Additionally, we evaluated a user adaptation methodology that incorporates user preference assessment and walking history data. "Acceptable detour time" was used as a measure of user preferences. As the primary plan, we suggested a method based on post-walking evaluations of route factors by the user, which was validated through an empirical walking experiment, highlighting the feasibility of our approach. However, this approach relies on user input for each route factor, raising concerns over user convenience. To address this issue, we extended our objective to devise a methodology that incorporates walking history, eliminating the need for user input. Drawing insights from an additional walking experiment, we formulated an alternative strategy for adjusting the acceptable detour time in our secondary plan. This strategy estimates the user's evaluation of each route factor by comparing the shortest route with the actual route walked. Application of this method to our experimental data demonstrated its utility and viability, but it also revealed an exacerbation of estimation error with updates in some situations, requiring further research to enhance the efficiency of our proposed methodology. [ABSTRACT FROM AUTHOR]

Built environment characteristics can greatly influence pedestrians' route choices with factors beyond distance, such as accessibility, convenience, safety, and aesthetics, playing crucial roles. Although current navigation apps, such as Google Maps and Waze, have successfully provided driving directions, their navigation services are insufficient and sometimes unrealistic for addressing pedestrians' needs, largely due to the lack of dedicated pedestrian networks and the associated navigation algorithms. To address the research gaps, this paper proposes a novel approach that integrates freely available geospatial data and computer vision technology to create a specialized inclusive network dataset for outdoor pedestrian navigation. Moreover, a pedestrian navigation algorithm is developed to generate more practical "shortest" and "alternative" paths by incorporating various sidewalk attributes. We applied the method to create a pedestrian navigation network in Las Vegas. SpaceNet's open imagery dataset was used to extract Las Vegas's road networks. A virtual audit process assessed the visual and operational properties of the sidewalk networks using Google street-level images, evaluating factors including sidewalk presence, widths, surface types and conditions, missing curb ramps, greenery, protection from weather conditions, and lighting. Google Earth's open elevation data were used to analyze road elevation profiles as meaningful 3D indicators of sidewalk accessibility for wheelchair users. Further, additional geometric properties of the network, including road curviness, proximity to road intersections, and directional changes, were detected and analyzed. A navigation experiment conducted with individuals having varying mobility abilities, including regular pedestrians, older adults, and wheelchair users demonstrated the effectiveness of the newly developed network and algorithm in meeting the diverse needs of pedestrians. • A specialized inclusive network dataset for outdoor pedestrian navigation. • A navigation algorithm generating more practical and realistic "shortest" and "alternative" pedestrian paths. • Test results indicated excellent path-finding performance of the deep learning-based graph. • Navigation experiment demonstrated the practicability of the newly developed pedestrian network and algorithm. • Roadway elevation profiles function as meaningful 3D indicators of sidewalk accessibility for wheelchair users. [ABSTRACT FROM AUTHOR]

Micro-Electro-Mechanical System (MEMS) inertial sensors, characterized by their small size, low cost, and low power consumption, are commonly used in foot-mounted wearable pedestrian autonomous positioning systems. However, they also have drawbacks such as heading drift and poor repeatability. To address these issues, this paper proposes an improved pedestrian autonomous 3D positioning algorithm based on dual-foot motion characteristic constraints. Two sets of small-sized Inertial Measurement Units (IMU) are worn on the left and right feet of pedestrians to form an autonomous positioning system, each integrated with low-cost, low-power micro-inertial sensor chips. On the one hand, an improved adaptive zero-velocity detection algorithm is employed to enhance discrimination accuracy under different step-speed conditions. On the other hand, considering the dual-foot gait characteristics and the height difference feature during stair ascent and descent, horizontal position update algorithms based on dual-foot motion trajectory constraints and height update algorithms based on dual-foot height differences are, respectively, designed. These algorithms aim to re-correct the pedestrian position information updated at zero velocity in both horizontal and vertical directions. The experimental results indicate that in a laboratory environment, the 3D positioning error is reduced by 93.9% compared to unconstrained conditions. Simultaneously, the proposed approach enhances the accuracy, continuity, and repeatability of the foot-mounted IMU positioning system without the need for additional power consumption.

The development of ubiquitous computing technology and the emergence of XR could provide pedestrian navigation with more options for user interfaces and interactions. In this work, we aim investigate the role of a mixed-reality map interface in urban exploration to enhance pedestrians' mental satisfaction. We propose a mixed-reality 3D minimap as a part of the navigation interface which pedestrians could refer to and interact during urban exploration. To further explore the different levels of detail of the map interface, we conducted a user study (n = 28, two groups with two tasks). We designed two exploratory activities as experimental tasks with two map modes (a normal one and a simplified one) to discuss the detailed design of the minimap interface. The results indicated that participants showed a positive attitude toward our method. The simplified map mode could result in a lower perceived workload in both tasks while enhancing performance in specific navigation, such as wayfinding. However, we also found that pedestrians' preference for the level of detail of the minimap interface is dynamic in navigation. Thus, we suggest discussing the different levels of detail further in specific scenarios. Finally, we also summarize some findings observed during user study for inspiring the study of virtual map interface of future mixed-reality navigation for urban exploration in various scenarios. [ABSTRACT FROM AUTHOR]

Unlike car navigation, where almost all vehicles can traverse every route, one route might not be optimal or even suitable for all pedestrians. Route geometry information, including tortuosity, twists and turns along roads, junctions, and road slopes, among others, matters a great deal for specific types of pedestrians, particularly those with limited mobility, such as wheelchair users and older adults. Offering practical routing services to these users requires that pedestrian navigation systems provide further information on route geometry. Therefore, this article proposes a novel method for extracting and analyzing the geometry properties of the shortest pedestrian paths, with a focus on open geospatial data across four aspects: (a) similarity, (b) route curviness, (c) road turns and intersections, and (d) road gradients. Deriving from the Hausdorff distance, a metric called the “dissimilarity ratio” was developed, allowing us to determine whether pairs of routes show any tendencies to be similar to each other. Using the “sinuosity index”, a segment-based technique quantified the route curviness based on the number and degree of the road turns along the route. Moreover, relying upon open elevation data, the road gradients were extracted to identify routes offering smoother motion and better accessibility. Lastly, the road turns and intersections were investigated as pedestrian convenience and safety indicators. A local government area of Greater Sydney in Australia was chosen as the case study. The analysis was implemented on OpenStreetMap (OSM) shortest pedestrian paths against Google Maps as a benchmark for real-world commercial applications. The similarity analysis indicated that over 90% of OSM routes were identical or roughly similar to Google Maps. In addition, while Spearman’s rank correlation showed a direct relationship between route curviness and route length, rS(758) = 0.92, p < 0.001, OSM, on average, witnessed more tortuous routes and, consequently, shorter straight roads between turns. However, OSM routes could be more suitable for pedestrians when the frequency of intersections and road slopes are at the center of attention. Finally, the devised metrics in this study, including the dissimilarity ratio and sinuosity index, showed their practicability in translating raw values into meaningful indicators.

The indoor navigation method shows great application prospects that is based on a wearable foot-mounted inertial measurement unit and a zero-velocity update principle. Traditional navigation methods mainly support two-dimensional stable motion modes such as walking; special tasks such as rescue and disaster relief, medical search and rescue, in addition to normal walking, are usually accompanied by running, going upstairs, going downstairs and other motion modes, which will greatly affect the dynamic performance of the traditional zero-velocity update algorithm. Based on a wearable multi-node inertial sensor network, this paper presents a method of multi-motion modes recognition for indoor pedestrians based on gait segmentation and a long short-term memory artificial neural network, which improves the accuracy of multi-motion modes recognition. In view of the short effective interval of zero-velocity updates in motion modes with fast speeds such as running, different zero-velocity update detection algorithms and integrated navigation methods based on change of waist/foot headings are designed. The experimental results show that the overall recognition rate of the proposed method is 96.77%, and the navigation error is 1.26% of the total distance of the proposed method, which has good application prospects. [ABSTRACT FROM AUTHOR]

High-precision indoor positioning technology is of great significance to the development of national economy. In recent years, with the increasing demand for location services and the continuous iteration of technology, indoor positioning continues to develop towards high precision and seamless. In the case that the satellite signal cannot cover the indoor, the high-precision indoor positioning technology has become a research hotspot, and a variety of positioning sources and corresponding positioning principles have been developed. Aiming at the latest development status of high-precision indoor positioning technology, we divide into high-precision positioning methods based on geometric relation, fingerprint matching, incremental estimation and quantum navigation according to different positioning principles. We introduce the positioning principle of various methods, discuss and analyze the current technology development, summary the characteristics of high-precision indoor positioning technology, and discuss the future development trend of multi-source, intelligent and popular. Through the analysis of different positioning principles and technologies, it has reference value for the future study of indoor high-precision positioning sources and fusion positioning methods of different positioning sources. [ABSTRACT FROM AUTHOR]

This paper is devoted to construction of reference walking trajectories for developing pedestrian navigation algorithms for smartphones. Such trajectories can be used both for verification of classical algorithms of navigation or for application of machine learning technics. Reconstruction of closed trajectories based on data from foot-mounted inertial measurement units (IMU) is investigated. The advantages of the approach are the use of inexpensive sensors and the simplicity of the presented method. We propose algorithms for reconstruction of smooth 2D pedestrian trajectories based on measurements from a single IMU as well as on combined measurements from two IMU’s. Introduced algorithms are based on application of modified Kalman filter with an assumption of IMU having zero velocity when foot contacts the ground. In case of two measurement units, it is additionally assumed that the positions of the sensors cannot differ significantly from each other. The algorithms were tested on trajectories lasting from 1 to 10 minutes, passing indoors on horizontal surfaces. Obtained results were compared with high precision trajectories acquired with GNSS RTK receivers. Additionally, the process of inter-device time synchronization is investigated and detailed description of the experiments and used equipment is given. The dataset used for verification of proposed algorithms is freely available at: http://gartseev.ru/projects/rtj2021.

Smartphone map-based pedestrian navigation is known to have a negative effect on the long-term acquisition of spatial knowledge and memorisation of landmarks. Landmark-based navigation has been proposed as an approach that can overcome such limitations. In this work, we investigate how different interaction technologies, namely smartphones and augmented reality (AR) glasses, can affect the acquisition of spatial knowledge when used to support landmark-based pedestrian navigation. We conducted a study involving 20 participants, using smartphones or augmented reality glasses for pedestrian navigation. We studied the effects of these systems on landmark memorisation and spatial knowledge acquisition over a period of time. Our results show statistically significant differences in spatial knowledge acquisition between the two technologies, with the augmented reality glasses enabling better memorisation of landmarks and paths.

Efficient shortest path algorithms are of key importance for routing and navigation systems. However, these applications are designed focusing on the requirements of motor vehicles, and therefore, finding paths in pedestrian sections of urban areas is not sufficiently supported. In addition, finding the shortest path is often not adequate for urban sidewalk routes, as users of these applications may also be interested in alternative routes that, although slightly longer, possess other desirable features and properties. According to the literature, the search for alternative routes is carried out mainly using the k-shortest paths (KSP) algorithm which represents an ordered list of all available alternatives. Even though various KSP algorithms have been proposed, to the best of our knowledge, there is no research addressing all issues inherent in a pedestrian navigation system. The purpose of this paper is to present a heuristic algorithm for graph datasets that implements a penalty-based method which, by increasing certain edge weights, effectively searches for the most accessible alternative paths in multi-route cases. To demonstrate how the algorithm works, we present experimental results on finding the most accessible paths in pedestrian sections of the historical center of Thessaloniki city. [ABSTRACT FROM AUTHOR]

Object: The influence of human factors on wayfinding performance is essential to improve the user experience of intelligent navigation service. Method: Based on four cognitive abilities, i.e. mental rotation, abstract reasoning, spatial short-term memory and spatial perception, this paper conducts several spatial cognitive experiments to analysis the correlation between spatial cognitive abilities and wayfinding performance. Results: The results indicate that: (1) These four spatial cognitive abilities are weakly correlated with each other, and can be separated partly with reference to the scale of recognition space. Specifically, cognitive group with mental rotation ability and abstract reasoning ability, and cognitive group with short ⁃ term memory ability and spatial perception ability, have stronger correlation respectively concerning the same space scale. While those groups where cognitive abilities cross different scale space have week correlation. (2) Mental rotation ability and abstract reasoning ability have higher significance and goodness of regression fitting on wayfinding performance. Considering the combined effect of multiple cognitive factors, in virtual scene, the mental rotation ability shows the highest influence while the spatial perception ability has the lowest influence; in real scene, the spatial perception ability shows the highest influence and the short-term memory ability has the lowest influence. (3) The wayfinding performance has significant scene effect with respect to the real and virtual scenes, i.e. the determinant coefficients of cognitive factors for way⁃ finding performance fitting varies with the scene type. Conclusions: These results provide an empirical reference for the effectiveness of pedestrian navigation research based on virtual scene. It can also serve as a basis for the design of navigation service considering human factors. [ABSTRACT FROM AUTHOR]

Voice-assisted digital maps have become mainstream navigation aids for pedestrian navigation. Although these maps are widely studied and applied, it is still unclear how they affect human behavior and spatial knowledge acquisition. In this study, we recruited thirty-three college students to carry out an outdoor wayfinding experiment. We compared the effects of voice-assisted digital maps with those of digital maps without voice instructions and paper maps by using eye tracking, sketch maps, questionnaires and interviews. The results show that, compared to the other map types, voice-assisted digital maps can help users reach their destinations more quickly and pay more attention to moving objects, thereby increasing the comfort levels of participants. However, the efficiency of voice-assisted maps on route memory tasks does not rival that of paper maps. Overall, the use of voice-assisted digital maps saves time but may reduce pedestrians' spatial knowledge acquisition. The results of this study reveal the influence of voice on pedestrian wayfinding and deepen the scientific understanding of the multimedia navigation mode in shaping human spatial ability. [ABSTRACT FROM AUTHOR]

Eye movement biometrics can enable continuous verification for highly secure environments such as financial transactions and defense establishments, as well as a more personalized and tailored experience in gaze-based human–computer interactions. However, there are numerous challenges to recognizing people in real environments using eye movements, such as implicity and stimulus independence. In the instance of wayfinding, this research intends to investigate implicit and stimulus-independent eye movement biometrics in real-world situations. We collected 39 subjects' eye movement data from real-world wayfinding experiments and derived five sets of eye movement features (the basic statistical, pupillary response, fixation density, fixation semantic and saccade encoding features). We adopted a random forest and performed biometric recognition for both identification and verification scenarios. The best accuracy we obtained in the identification scenario was 78% (equal error rate, EER = 6.3%) with the 10-fold classification and 64% (EER = 12.1%) with the leave-one-route-out classification. The best accuracy we achieved in the verification scenario was 89% (EER = 9.1%). Additionally, we tested performance across the 5 feature sets and 20 time window sizes. The results showed that the verification accuracy was insensitive to the increase in the time window size. These findings are the first indication of the viability of performing implicit and stimulus-independent biometric recognition in real-world settings using wearable eye tracking. [ABSTRACT FROM AUTHOR]

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The last years have seen a growing body of literature on data-driven pedestrian inertial navigation. However, despite this, it is still unclear how to efficiently combine classical models and other a priori information with existing machine learning frameworks. In this paper, we first categorize existing approaches to data-driven pedestrian inertial navigation, including approaches where a machine learning algorithm is embedded into an overarching classical framework and purely data-driven frameworks. We then propose an estimation framework where navigation estimates obtained by classical means are fed to a machine learning algorithm which is trained to correct and improve the estimates. Further, we describe three symmetries that can be used to constrain the proposed estimation framework and thereby improve its performance. These are 1) the rotational symmetry of pedestrian dynamics, 2) the rotational symmetry of the sensors, and 3) the temporal symmetry of pedestrian dynamics. To demonstrate the usefulness of the proposed framework, we use data from foot-mounted inertial sensors utilizing zero-velocity updates under mixed walking and running. Machine learning corrections are implemented using both neural networks and Gaussian processes. [ABSTRACT FROM AUTHOR]

Foot-mounted positioning devices are becoming more and more popular in the different application field. For example, inertial sensors are now embedded in safety shoes to monitor security. They allow positioning with zero velocity update to bound the error growth of foot-mounted inertial sensors. High positioning accuracy depends on robust zero velocity detector (ZVD). Existing Artificial Intelligent (AI)-based methods classify the pedestrian dynamics to adjust ZVD at the cost of high computation costs and error propagation from miss-classification. We propose a machine learning model to detect zero velocity moments without any pre-classification step, named Uniform AI Model for All pedestrian Motions (UMAM). Performance is evaluated by benchmarking on two new subjects of opposite gender and different size, not included in the training data set, over complex indoor/outdoor paths of 2 km for subject 1 and 2.1 km for subject 2. We obtain an average 2D loop closure error of less than 0.37%. [ABSTRACT FROM AUTHOR]

Magnetic field signals are ubiquitous, stable, and have little effect on the human body. Due to these qualities magnetic field matching (MFM) has become a popular indoor positioning method and has attracted considerable attention within the smartphone geolocation community. However, the need to frequently calibrate the magnetometer bias seriously undermines the applicability and stability of MFM. In this paper we present a magnetometer bias insensitive MFM based on pedestrian dead reckoning (PDR) for smartphones in the indoor environment. An inertial navigation system based PDR is designed for generating the relative trajectory and attitude. The relative trajectory is used to correlate the magnetic field feature time sequence to improve the distinguishability and the attitude is used for projecting the reference magnetic field feature from ${n}$ -frame to ${b}$ -frame, to perform the MFM in the ${b}$ -frame to eliminate the influence of the magnetometer bias. The results of eight experiments in four smartphones showed that the proposed method could effectively eliminate the influence of the magnetometer’s bias in the MFM, and finally reached mean positioning accuracy of 0.77 m (RMS). [ABSTRACT FROM AUTHOR]

Robust and accurate zero-velocity detection can improve the performance of zero-velocity-aided foot-mounted inertial navigation system. To ensure the accuracy of zero-velocity detection, we propose a novel detector based on contrastive learning. This detector roughly eliminates the inertial data that must not be the zero-velocity event in advance, to reduce the computation cost. Then the detector uses the remaining inertial data to detect the zero-velocity event via a trained contrastive neural network. The contrastive neural network uses the triplet network and is trained by comparing with the anchor data which consists of the known static inertial data from the period of initial alignment. The classifier will finally determine whether the output of the triplet network is the zero-velocity event. Two experiments were conducted to evaluate this novel detector, showing that it can adaptively and accurately detect the zero-velocity event. The horizontal position errors of the two experiments are respectively 1.33m over a 953m outdoor path with walking and 3.74m over a 1143m indoor/outdoor path with combined motion of low dynamic and high dynamic. [ABSTRACT FROM AUTHOR]

State-of-the-art mobile pedestrian navigation systems often employ GPS or other positioning methods for continuous tracking of users, and thus provide them with in-situ turn-by-turn route guidance along a desired route. However, studies have shown that user experience and acquisition of spatial knowledge decrease due to the "blind" following of such turn-by-turn navigation systems. This paper proposes a novel interface concept for mobile pedestrian navigation systems that provide navigation guidance without restricting the users to a predefined route. Specifically, the proposed novel user interface was based on the concept of the Potential Route Area (PRA), which defines a dynamic area consisting of all potential routes not longer than a certain detour the user is willing to accept. Within that area, the user can freely choose his/her own route and alter it anytime, and can still arrive at the destination within the desired detour tolerance. As a proof of concept, the proposed PRA-based system was then tested against a conventional turn-by-turn navigation system, represented by Google Maps, in a real-world navigation experiment, which revealed that the acquisition of spatial knowledge and user experience were substantially improved when using the PRA-based system. This can be explained by the fact that the PRA-based system provides its users with more freedom in choosing their own route. [ABSTRACT FROM AUTHOR]

Smartphone-based inertial and magnetic sensors can be the basis for pedestrian navigation, whenever external positioning signals are limited or unavailable. Such navigation solutions are typically accomplished by a practice known as pedestrian dead reckoning, wherein the step length and the heading angle are estimated to form the horizontal trajectory of the user. One of the main challenges in these methods is the unknown misalignment between the user’s forward axis and the device’s frame, which imposes a great difficulty in finding the user’s heading. In this article, the problem of estimating the walking direction is addressed by a learning-based approach. Specifically, a novel deep network architecture is designed for extracting the motion vector in the device coordinates using accelerometer measurements. It consists of temporal convolutions and multiscale attention layers and involves a rotation-invariant property that is analytically derived. The network model is integrated with a geometric calculation, employing gravity and geomagnetic directions, to convert the motion vector into the heading angle relative to north. Extensive experiments of natural walking activity were conducted by a single pedestrian, with smartphones placed freely in various pockets. These were used for training the proposed model, in a user-specific approach. Finally, the entire framework was evaluated on unseen data, comparing the deep network against a mechanistic baseline method. Using the proposed network, with merely 5500 parameters, the resulting heading errors had a median value of 9.8°, which was lower by 2.5° compared with the baseline method. [ABSTRACT FROM AUTHOR]