Pedestrian Navigation Applications Research Articles

An Enhanced Hidden Markov Model for Map-Matching in Pedestrian Navigation

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.

The impact of progress indicators and information density on users' temporal perception and user experience in mobile pedestrian navigation applications

This study examines progress indicators and information density in mobile pedestrian navigation applications and explores their impact on user duration perception and experience. The study attempted to improve the user's pedestrian navigation experiences through different interface visual design strategies. We conducted a 3 × 2 between-subjects analysis to evaluate the effect of progress indicators (bar, pointer, and countdown) and visual information (less and regular information) in a simulated pedestrian navigation application. Forty-eight subjects participated in a one-on-one manipulation experiment simulating walking a designated route, followed by duration judgments and scale surveys. The results showed that progress indicator types significantly influenced participants' duration perceptions. The duration perception of “less information” increased by nearly 26.3%, and the duration perception of “regular amount of information” decreased by almost 29%. In addition, participants experienced the progress “bar” type as having higher learnability. Finally, participants preferred the “bar” type progress indicator, and the information density did not affect their preferences.

A Training Smartphone Application for the Simulation of Outdoor Blind Pedestrian Navigation: Usability, UX Evaluation, Sentiment Analysis.

Training blind and visually impaired individuals is an important but often neglected aspect of Assistive Technology solutions (ATs) that can benefit from systems utilizing multiple sensors and hardware devices. Training serves a dual purpose as it not only enables the target group to effectively utilize the ATs but, also, helps in improving their low acceptance rate. In this paper, we present the design, implementation, and validation of a smartphone-based training application. It is a form of immersive system that enables users to learn the features of an outdoor blind pedestrian navigation application and, simultaneously, to help them develop long-term Orientation and Mobility (O&M) skills. The system consists of an Android application leveraging, as data sources, an external high-accuracy GPS sensor for real-time pedestrian mobility tracking, a second custom-made device attached to traffic lights for identifying their status, and an ultra-sonic sensor for detecting near-field obstacles on the navigation path of the users. The training version running as an Android application employs route simulation with audio and haptic feedback, is functionally equivalent to the main application, and was used in the context of specially designed user-centered training sessions. A Usability and User Experience (UX) evaluation revealed the positive attitude of the users towards the training version as well as their satisfaction with the skills acquired during their training sessions (SUS = 69.1, UEQ+ = 1.53). Further confirming the positive attitude was the conduct of a Recursive Neural Network (RNN)-based sentiment analysis on user responses with a score of 3 on a scale from 0 to 4. Finally, we conclude with the lessons learned and the proposal of general design guidelines concerning the observed lack of accessibility and non-universal interfaces.

Recent Advances in Pedestrian Inertial Navigation Based on Smartphone: A Review

Indoor location-based service is a hot research topic whose application market size is expected to grow from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\$ $ </tex-math></inline-formula> 41 billion by 2022 and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\$ $ </tex-math></inline-formula> 58 billion by 2023. As a portable communication device, smartphones have inherent advantages (such as being an essential gadget in daily life, embedding various sensors, and having powerful computing and storage ability), which provides a great opportunity for human activity recognition and pedestrian navigation. Smartphone-based pedestrian inertial navigation systems (PINSs) utilize measurements from inertial sensors embedded in smartphones to reckon the pedestrian’s location. Compared with other indoor position technologies, PINS has the advantage of autonomy and continuity, and can achieve high-precision positioning in a short period. Though various PINS methods based on the smartphone have been proposed, up-to-date review papers that summarize relevant technologies, methods, and solutions of PINS are relatively less so far in the literature. This article aims to provide an elaborated, timely, and valuable survey of different infrastructure-free pedestrian positioning and navigation techniques, including pedestrian inertial navigation methods, performance evaluation, and applications. Finally, current challenges and future research trends are discussed. This work propels a better understanding of existing pedestrian inertial positioning methods. It is helpful for researchers to further design and develop more accurate and robust pedestrian positioning and navigation systems based on inertial sensors embedded in the smartphone.

Use and Perceptions of Pedestrian Navigation Apps: Findings from Bologna and Porto

Pedestrian Navigation Applications (PNAs) provide assistance in terms of self-localization, space recognition, and turn-by-turn navigation. The use, motivations and perceptions associated with these applications have been under investigated due to users being insufficiently involved in their design and development. This paper analyses the extent to which PNAs are used for assisting people to walk, the frequencies and reasons of using these applications, the perceptions about them, and the barriers preventing them from being used. The study is supported by a questionnaire (N = 1438) that was administered in Bologna (Italy) and Porto (Portugal). Results indicated that 42% of the respondents use PNAs mainly on an occasional basis to find locations and the shortest routes. Google Maps was the preferred navigation service. Statistical tests showed that PNAs were more likely to be used by younger adults and students. The lack of need was the main reason for not using these apps, due to the good spatial knowledge of the cities or the non-use of the pedestrian mode for regular trips. Respondents would like to have apps that are more accurate, usable, and adjusted to pedestrian navigation. The findings described in this paper could be helpful for future designs of PNAs, especially to match pedestrian needs more effectively and to enhance the role of these apps in promoting healthier and sustainable lifestyles.

AR-Based Navigation Using RGB-D Camera and Hybrid Map

Current pedestrian navigation applications have been developed for the smartphone platform and guide users on a 2D top view map. The Augmented Reality (AR)-based navigation from the first-person view could provide a new experience for pedestrians compared to the current navigation. This research proposes a marker free system for AR-based indoor navigation. The proposed system adopts the RGB-D camera to observe the surrounding environment and builds a point cloud map using Simultaneous Localization and Mapping (SLAM) technology. After that, a hybrid map is developed by integrating the point cloud map and a floor map. Finally, positioning and navigation are performed on the proposed hybrid map. In order to visualize the augmented navigation information on the real scene seamlessly, this research proposes an orientation error correction method to improve the correctness of navigation. The experimental results indicated that the proposed system could provide first-person view navigation with satisfactory performance. In addition, compared to the baseline without any error correction, the navigation system with the orientation error correction method achieved significantly better performance. The proposed system is developed for the smart glasses and can be used as a touring tool.

Influence of Viewing Field on Zoom Levels in Pedestrian Orientation Task Using Smartphones

Abstract. Psychological findings indicate that the scale of human perception has implications on optimal map design. According to the map-based orientation in the real environment, this viewing scale depends on the visual field and is graphically reproduced using zoom levels, which significantly influences the map display area on smartphones. However, it is still unclear how to determine these zoom levels in the pedestrian navigation application. The purpose of this article is to adapt the map display area to the location-related viewing field using a corresponding zoom level. This optimal map display area should make it easier for the pedestrians’ self-location and navigational decisions. The results of the experiments have shown that there was a close relationship between the viewing field and the zoom level on the smartphone. However, if the first decision point of changing direction was in the viewing field, the distance between the viewpoint (You-Are-Here point) and this decision point influenced the zoom level. Otherwise, this distance did not have any influence on the zoom level. In this case, the distance between the viewpoint and the local landmarks determined the zoom level.

Estimation of Pedestrian Altitude Inside a Multi-Story Building Using an Integrated Micro-IMU and Barometer Device

This paper presents a novel method of estimating the altitude of pedestrians who are walking in the indoor environment of a multi-story building. We will show that to achieve pedestrian altitude estimation, a pedestrian only needs to wear a small MEMS-based integrated sensing device consisting of a micro-IMU (i.e., consists of a 3D accelerometer, a 3D gyroscope, and a 3D magnetometer) and a barometer, during indoor activities. High-precision estimates of the pedestrian's position in the vertical direction were obtained by utilizing the acceleration and angular rate data, as well as the height, deduced from barometer data. The inherent drifts of the IMU sensors, which lead to cumulative errors in altitude estimation, were sharply reduced using a complementary filter and an error compensation algorithm. The experimental results demonstrate that this method is effective in reducing estimation errors. When a person walks on stairs with the same step height, the error of the estimated height of each step is within 0.5 cm, and the cumulative height error is about 1.7% over a total height of 2.9 m. This integrated sensing device also exhibits good stability, i.e., three 20-min tests in a 12-h period showed that the cumulative error accounts for about 2% of the total height of 11.23 m. When the stairs have different heights, i.e., heights ranging from 12 to 28 cm, the estimated height error of each step is within 2 cm. With an ability to provide accurate and reliable vertical altitude estimates of pedestrians inside a multi-story building, the sensing device developed through this paper is suitable for use in 3D-space body tracking and pedestrian navigation applications.

Integrating Moving Platforms in a SLAM Agorithm for Pedestrian Navigation.

In 3D pedestrian indoor navigation applications, position estimation based on inertial measurement units (IMUss) fails when moving platforms (MPs), such as escalators and elevators, are not properly implemented. In this work, we integrate the MPs in an upper 3D-simultaneous localization and mapping (SLAM) algorithm which is cascaded to the pedestrian dead-reckoning (PDR) technique. The step and heading measurements resulting from the PDR are fed to the SLAM that additionally estimates a map of the environment during the walk in order to reduce the remaining drift. For integrating MPs, we present a new proposal function for the particle filter implementation of the SLAM to account for the presence of MPs. In addition, a new weighting function for features such as escalators and elevators is developed and the features are learned and stored in the learned map. With this, locations of MPs are favored when revisiting the MPs again. The results show that the mean height error is about 0.1 m and the mean position error is less than 1 m for walks with long distances along the floors, even when using multiple floor level changes with different numbers of floors in a multistory environment. For walks with short walking distances and many floor level changes, the mean height error can be higher (about 0.5 m). The final floor number is in all cases except one correctly estimated.

Fusion of 3D GIS, Vision, Inertial and Magnetic Data for Improved Urban Pedestrian Navigation and Augmented Reality Applications

n the context of pedestrian navigation and Augmented Reality applications in urban environments, we propose to fuse the pose estimated through a vision process, thanks to a precisely known 3D model, with inertial and magnetic measurements. First, this allows for updating a Pedestrian DeadReckoning process and improving the positioning accuracy. Second, a trusted pose estimate allows us to reproject 3D Geographical Information System content in Augmented Reality with qualified confidence. Because 3D Geographical Information System data are provided by many sources inducing an inhomogeneous precision and level of quality, being able to qualify these 3D contents is important to validate their relevance to use them. A long pedestrian path of 3 km in an urban environment with a sparsely known 3D model of urban furniture was conducted. This has permitted validation of the contribution of sensor fusion that improves the positioning accuracy and allows characterization of the 3D Geographical Information System content directly onsite using Augmented Reality. Performance is presented in terms of positioning accuracy in urban spaces. © 2018 Institute of Navigation.

A Semi-Automated Method to Generate GIS-Based Sidewalk Networks for Asset Management and Pedestrian Accessibility Assessment

Pedestrian infrastructure that is comfortable, connected to destinations of interest, and accessible to those with disabilities is vital to a safe, accessible, equitable, and sustainable transportation system. Planners recognize the benefits of providing well-maintained sidewalks and curb ramps, but often lack the asset management systems necessary to inventory sidewalk maintenance problems, prioritize sidewalk maintenance needs, and track the implementation of sidewalk improvement projects. Communities that are managing sidewalk presence and condition data typically link the data to their roadway network, which makes tracking specific sidewalk assets difficult. This paper introduces an affordable, semi-automated, and easy-to-implement process to generate a GIS-based sidewalk network with associated links and nodes representing crosswalks and intersections. Quantitative sidewalk condition data can be loaded onto the network, which allows it to be used to manage sidewalks as transportation assets, assessing pedestrian accessibility, prioritizing repairs or improvements, and to automatically identify accessible routes between origins and destinations. System inputs include parcel-level land-use and roadway centerline data, both of which are publicly available and free in most cases. The network is generated within the ArcGIS environment, using Python scripts to implement embedded ArcGIS functions. The method requires few computational resources, and tremendously reduces the manual labor required to develop a fully interconnected sidewalk network. Examples from multiple communities are presented to show how quantitative sidewalk condition data are loaded onto the network, and illustrate the network’s potential for pedestrian navigation and routing applications.

Towards a Landmark-Based Pedestrian Navigation Service Using OSM Data

With the advent of location-aware smartphones, the desire for pedestrian-based navigation services has increased. Unlike car-based services where instructions generally are comprised of distance and road names, pedestrian instructions should instead focus on the delivery of landmarks to aid in navigation. OpenStreetMap (OSM) contains a vast amount of geospatial information that can be tapped into for identifying these landmark features. This paper presents a prototype navigation service that extracts landmarks suitable for navigation instructions from the OSM dataset based on several metrics. This is coupled with a short comparison of landmark availability within OSM, differences in routes between locations with different levels of OSM completeness and a short evaluation of the suitability of the landmarks provided by the prototype. Landmark extraction is performed on a server-side service, with the instructions being delivered to a pedestrian navigation application running on an Android mobile device.

Integrating Open Spaces into OpenStreetMap Routing Graphs for Realistic Crossing Behaviour in Pedestrian Navigation

Map data for pedestrian routing and navigation provided by OpenStreetMap is getting more and more detailed, but current approaches often fail to take advantage of available information. This paper addresses the issue of integrating open spaces, such as squares and plazas, into pedestrian routing graphs to support realistic crossing behaviour. We evaluate different approaches to solving this issue, including skeletonization algorithms as well as approaches for wayfinding in digital worlds, and recommend that – for pedestrian navigation applications – the visibility graph approach should be preferred over the commonly-used medial axis or straight skeleton approaches, since it provides direct routes, which are more realistic and better suited for pedestrian routing applications.

PDR/INS/WiFi Integration Based on Handheld Devices for Indoor Pedestrian Navigation

Providing an accurate and practical navigation solution anywhere with portable devices, such as smartphones, is still a challenge, especially in environments where global navigation satellite systems (GNSS) signals are not available or are degraded. This paper proposes a new algorithm that integrates inertial navigation system (INS) and pedestrian dead reckoning (PDR) to combine the advantages of both mechanizations for micro-electro-mechanical systems (MEMS) sensors in pedestrian navigation applications. In this PDR/INS integration algorithm, a pseudo-velocity-vector, which is composed of the PDR-derived forward speed and zero lateral and vertical speeds from non-holonomic constraints (NHC), works as an update for the INS to limit the velocity errors. To further limit the drift of MEMS inertial sensors, trilateration-based WiFi positions with small variances are also selected as updates for the PDR/INS integrated system. The experiments illustrate that positioning error is decreased by 60%–75% by using the proposed PDR/INS integrated MEMS solution when compared with PDR. The positioning error is further decreased by 15%–55% if the proposed PDR/INS/WiFi integrated solution is implemented. The average accuracy of the proposed PDR/INS/WiFi integration algorithm achieves 4.5 m in indoor environments.

Personal Navigation Algorithms Based on Wireless Networks and Inertial Sensors

Abstract The work aims at a development of positioning algorithm suitable for low-cost indoor or urban pedestrian navigation application. The sensor fusion was applied to increase the localization accuracy. Due to required low application cost only low grade inertial sensors and wireless network based ranging were taken into account. The wireless network was assumed to be preinstalled due to other required functionality (for example: building control) therefore only received signal strength (RSS) range measurement technique was considered. Wireless channel loss mapping method was proposed to overcome the natural uncertainties and restrictions in the RSS range measurements The available sensor and environment models are summarized first and the most appropriate ones are selected secondly. Their effective and novel application in the navigation task, and favorable fusion (Particle filtering) of all available information are the main objectives of this thesis.

Seamless outdoor/indoor navigation with WIFI/GPS aided low cost Inertial Navigation System

This paper describes an integrated navigation system that can be used for pedestrian navigation in both outdoor and indoor environments. With the aid of Global Positioning System (GPS) positioning solutions, an Inertial Navigation System (INS) can provide stable and continuous outdoor navigation. When moving indoors, WIFI positioning can replace the GPS in order to maintain the integrated system’s long-term reliability and stability. On the other hand, the position from an INS can also provide a priori information to aid WIFI positioning. Signal strength-based WIFI positioning is widely used for indoor navigation. A new fingerprinting method is proposed so as to improve the performance of WIFI stand-alone positioning. For pedestrian navigation applications, a step detection method is implemented to constrain the growth of the INS error using an Extended Kalman Filter (EKF). Experiments have been conducted to test this system and the results have demonstrated the feasibility of this seamless outdoor/indoor navigation system.

An efficient and robust maneuvering mode to calibrate low cost magnetometer for improved heading estimation for pedestrian navigation

Personal navigation systems intend to provide the navigation information in any environment, indoors and outdoors, and at any time. In outdoor environments, the positioning solution is typically provided by using Global Positioning System (GPS). However, GPS is inaccurate or unavailable in most of indoor environments and therefore other externally-referenced sensing techniques are required. Inertial sensing techniques are used for pedestrian navigation in association with dead reckoning approach. Magnetometers can be used to derive the user’s heading by sensing the Earth’s magnetic field. In this paper, an efficient and robust maneuvering mode to calibrate low cost magnetometer is recommended for pedestrian navigation applications. Additionally, other maneuvering modes and errors associated with each mode to achieve best estimation for the calibration parameters in the 3D Space are also provided. Also, the effect of using different maneuvering modes (DMM) on the heading estimation for the pedestrian navigation is studied. The results show that the coordinated mode is suitable to perform the calibration process as the unit is rotated in a way to cover the whole 3D space.

Pedestrian navigation using the sense of touch

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 user device such as a mobile phone. Historically, human–computer interaction has been visual, data, or images on a screen. Haptic feedback can be an important modality in Mobile Location-Based Services like – knowledge discovery, pedestrian navigation and notification systems. In this paper we describe a methodology for the implementation of haptics in four distinct prototypes for pedestrian navigation. Prototypes are classified based on the user’s navigation guidance requirements, the user type (based on spatial skills), and overall system complexity. Here haptics is used to convey location, orientation, and distance information to users using pedestrian navigation applications. Initial user trials have elicited positive responses from the users who see benefit in being provided with a “heads up” approach to mobile navigation. We also tested the spatial ability of the user to navigate using haptics and landmark images based navigation. This was followed by a test of memory recall about the area. Users were able to successfully navigate from a given origin to a Destination Point without the use of a visual interface like a map. Results show the users of haptic feedback for navigation prepared better maps (better memory recall) of the region as compared to the users of landmark images based navigation.

A multiobjective model for generating optimal landmark sequences in pedestrian navigation applications

Landmarks provide the most predominant navigational cue for pedestrian navigation. The choice and representation of landmarks require an optimal approach to meet the needs of pedestrians, for example, shorter distances, fewer turns, and easy confirmation. This article proposes a multiobjective model to generate optimal landmark sequences for pedestrian route instructions. This model offers a general approach to meet the diverse needs of pedestrians. A modified ant colony optimization (ACO) algorithm is used to implement the proposed model. This research determined the parameters of the modified ACO algorithm by testing the whole study area using various weight combinations. This article also discusses the optimization process for various cases, for example, different origin–destination (OD) pairs, different pedestrian study areas, the influence of landmark density, and the overall performance comparison in achieving four objectives. Experimental results have confirmed that this proposed model can optimize landmark sequences for pedestrian route instructions.