Mobile Navigation Research Articles

Landmarks based route planning and linear path generation for mobile navigation applications

In this study a landmarks based navigation model considering local context is designed and a prototype is implemented. A route planning model where both the distance of a route and the strength of landmarks along the route are considered when deciding the optimum route to follow is developed. The Dijkstra algorithm is used as the basis and if the route is having more strength related to landmarks, it is prioritize while comparing with the distance. The route’s strength is defined based on number of landmarks visible along it and their salience. The horizontal spread, the height and the visibility of landmarks at different time of the day and the social/cultural significance are considered as attributes which give strength to a landmark. To utilize the mobile screen in effective manner, a map generalization is used to show the optimum path. With landmarks the map becomes congested and difficult to read on mobile screen. Therefore the path is reduced to a linear map which shows the path reducing curves while emphasizing the turns. Douglas–Peucker algorithm is used to derive the linear path. The linear map, which is a schematic representation of the actual map, reduces the path between junctions to straight lines and thereby uses less screen area. The implementation is done using mobile web approach and web services to reduce the platform dependency. OpenStreetMap is used as the main data source with sample local landmarks. All technologies used are free and open source. The application is still being verified and tested in Sri Lanka.

Vision-Based Real-Time Positioning and Autonomous Navigation System Using Mobile Robot in Indoor Environments

Research toward unmanned mobile robot navigation has gained significant importance in the last decade due to its potential applications in the location-based services industry. The increase in construction of large space indoor buildings has made difficulty for humans to operate within such environments. In this study, a mobile robot's indoor navigation algorithm is developed with vision cameras. Using two monocular cameras (one looking forward and one looking downward), the developed algorithms make use of the salient features of the environments to estimate rotational and translational motions for real-time positioning of the mobile robot. At the same time, an algorithm based on artificial landmark recognition is developed. The artificial landmark is shaped arrow based signboards with different colors representing different paths. These algorithms are integrated into a designed framework for mobile robot real-time positioning and autonomous navigation. Experiments are performed to validate the designed system using the mobile robot PIONEER P3-AT. The developed algorithm was able to detect and extract artificial landmark information up to 3 m distance for the mobile robot guidance. Experiment results show an average error of 0.167 m deviation from the ideal path, signified the good ability and performance of the development autonomous navigation algorithm.

Stress Supports Spatial Knowledge Acquisition during Wayfinding with Mobile Maps

GIScience 2016 Short Paper Proceedings Stress Supports Spatial Knowledge Acquisition during Wayfinding with Mobile Maps P. Frei 1 , K.-F. Richter 1 , S. I. Fabrikant 1 Geography Department, University of Zurich Winterthurerstrasse 190, 8057 Zurich, Switzerland {patrice.frei|kai-florian.richter|sara.fabrikant}@geo.uzh.ch Abstract We detail a novel empirical approach with in-situ psycho-physiological measurements to assess how stress influences spatial knowledge acquisition, mobile map use, and wayfinding success when performing a navigation task in an unknown urban environment. We recorded pedestrians’ navigation trajectories, mobile map interactions, eye movements, and galvanic skin responses, with varying stress conditions. Our results clearly indicate that stress supports navigators in forming good survey knowledge, possibly due to enhanced engagement. This seems to emerge from their goal-oriented interaction with the mobile map. Our study results contradict earlier findings, contribute to the on-going debate whether using mobile navigation systems are harmful for humans’ capacity to acquire environmental knowledge during navigation, and highlight the important influence of people’s individual spatial abilities and emotional states on their knowledge acquisition. 1. Introduction Does spatial knowledge acquisition deteriorate when people rely on mobile navigation assistance, as prior studies suggest (e.g., Gardony et al. 2013)? If yes, does it relate to disengagement from the navigated environment (Leshed et al. 2008), and/or from the wayfinding decision-making process (Bakdash et al. 2008)? These questions motivated our study. As increasing empirical evidence suggests that navigation performance can be predicted by varying individual differences, for example, spatial abilities (Hegarty et al. 2002), personality traits, such as anxiety (Thoresen et al. 2016), or emotional states, such as stress (Wilkening and Fabrikant 2011), we wondered how individual differences might interact with spatial knowledge acquisition and mobile map use. 2. Methods Thirtyfive members (f:2; m:33) of the Swiss Armed Forces International Command (SWISSINT) participated in our study. Before the experiment participants completed a demographic questionnaire, the Santa Barbara Sense of Direction Test (in German), the perspective-taking/spatial orientation test, and the building memory test. We divided participants into two (‘stress’ | ‘control’) groups by median-split on their perspective-taking test results, such that each group contained the same number of ‘high’ and ‘low’ performing participants. Participants were asked to navigate from a start to an end location along five waypoints in a given sequence, in an environment unfamiliar to them. They were given a mobile map application running on a tablet computer, which displayed start and end locations, the waypoints, and participants’ current (GPS) position, but no prescribed routes. Participants wore a mobile eye tracker connected to a laptop carried in a backpack, and a wrist-band 1 that recorded various psycho-physiological signals (e.g., galvanic skin responses http://bodymonitor.de/smartband/

Walk and Learn: An Empirical Framework for Assessing Spatial Knowledge Acquisition during Mobile Map Use

GIScience 2016 Short Paper Proceedings Walk and Learn: An Empirical Framework for Assessing Spatial Knowledge Acquisition during Mobile Map Use A. Brugger 1 , K.-F. Richter 1 , S. I. Fabrikant 1 Department of Geography, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland {annina.bruegger; kai-florian.richter; sara.fabrikant}@geo.uzh.ch Abstract We gladly use automated technology (e.g., smart devices) to extend our hard working minds. But what if such technology turns into mind crutches we cannot do without? Understanding how varying levels of automation in mobile maps might impact navigation performance and spatial knowledge acquisition will provide important insights for the ongoing debate on the potentially detrimental effects of using navigation systems on human spatial cognition. We need to identify the right balance between system automation (support) and user autonomy (self-reliance). Preliminary results of a pilot study performed within a novel empirical framework indicate that it is possible to increase user autonomy and spatial knowledge acquisition without negatively impacting navigation performance and usefulness of the system. 1. Introduction Various research fields have investigated how system automation might influence human knowledge and abilities. It is generally agreed that computers often make decisions originally performed by humans in a (more) efficient way. This has positive and negative impacts on humans as, for example, automation can reduce their physical and cognitive effort (Sheridan 2002). This also holds for navigational tasks, where acquiring spatial knowledge is crucial to orient and move in space without getting lost. Recent developments in self-driving vehicles highlight the need for better understanding human behavior, especially when humans have to take over from automated systems during system failure (Merat et al. 2014). The ideal human- system interaction would be to use the best of both human and technology (Sheridan 2002), which we aim for in our research. Specifically, how do we balance the advantages of system automation and the need for human autonomy to maximize both navigation efficiency and knowledge acquisition? 2. Balancing Assistance and Engagement Research investigating mobile navigation aids identified negative impacts on spatial knowledge acquisition, despite being very effective for efficient navigation (e.g. Willis et al. 2009). The consequences of automated guidance seem to be a disengagement of navigators’ attention from their surroundings (Gardony et al. 2013), and split attention between mobile device and the traversed environment (Willis et al. 2009). However, mobile navigation devices should enable pro-active engagement with the environment, which will lead to better spatial knowledge acquisition (Chung et al. 2016; Parush et al. 2007), as systems might break down, or users might lose the device and suddenly depend on their own abilities (Hirtle and Raubal 2013). The means to design such systems are yet unclear. Systems would need to provide efficient wayfinding support (sufficient system

Real-Time Tracking and Navigation for Magnetically Manipulated Untethered Robot

Magnetically manipulated untethered robot, such as an active wireless capsule endoscope, has shown great potential for controlled inspection inside the gastrointestinal tract. To enable the effective manipulation of the robot, real-time pose (position and orientation) information of the robot must be obtained as an important sensory feedback. Usually, a magnetic tracking method is used to provide the pose information. Due to the magnetic disturbance, a traditional magnetic tracking method cannot work simultaneously with the magnetic manipulation system. In this paper, a simultaneously tracking and navigation method is proposed to realize a closed-loop control of the magnetically manipulated untethered robot. The main approach is to conduct a multi-object tracking of the involved magnetic objects. With the proposed method, real-time pose information can be estimated during the manipulation, and both the tracking and manipulation are carried out by a magnetic manner. Therefore, the mobile navigation of the magnetically manipulated untethered robot can be achieved by using a pre-defined map. Experimental results verified the proposed method, and a mean position error of 1.2 mm for the passive magnet has been obtained.

雨天におけるLIDARを用いたナビゲーションを可能とする自律移動ロボットの開発

A LIDAR is commonly used for autonomous mobile navigation. It detects raindrops under rainy situations since its output laser beams collide the drops. This influences against performance of the navigation, for example obstacle avoidance. In this study, we first investigate influence of rain against the LIDAR and then propose a coping method, which contains two ways, for reducing the rain's influence. The first way is a hardware-based coping method that is to add additional waterproof parts. The second way is a software-based coping method that is to remove raindrops from the LIDAR readings like a filtering algorithm. By using these methods, number of raindrop detection is significantly reduced. As a result, autonomous navigation can be performed in rainy situations and its performance is verified in a man-made environment.

Constructing a Routable Transit Network from a Real-time Vehicle Location Feed

GIScience 2016 Short Paper Proceedings Constructing a Routable Transit Network from a Real-time Vehicle Location Feed Nate Wessel 1 , Jeff Allen 1 , Steven Farber 2 Department of Geography and Planning, University of Toronto, Sidney Smith Hall, 100 St. George Street, Toronto, ON M5S 3G3, Canada Email: {nate.wessel; jeff.allen}@mail.utoronto.ca Department of Human Geography, University of Toronto Scarborough, 1265 Military Trail, Toronto, ON M1C 1A4, Canada Email: steven.farber@utoronto.ca Abstract Many transit agencies publish open access feeds of real-time fleet locations for use by application developers to deliver mobile navigation services. However, by collecting a transit data feed over time, we demonstrate how to recreate retrospective, routable transit networks that are useful in answering network performance and accessibility research questions. In this research we develop an end-to-end GIS toolchain for 1) downloading and storing a transit data feed, 2) coercing the collected spatiotemporal database into a retrospective transit schedule adhering to the General Transit Feed Specification (GTFS) data standard, and 3) creating a routable transit network with time-dependent travel times in OpenTripPlanner. We further demonstrate how this toolchain can be used to identify discrepancies between scheduled and actual travel times on the network and motivate the usefulness of this approach through an accessibility analysis. 1. Introduction The emergence of the General Transit Feed Specification (GTFS) data standard, and the publication of GTFS packages by thousands of agencies worldwide has unleashed a flurry of tools development for researching transit networks (Hadas and Ranjitkar 2012), transit travel times (Farber et al. 2014), and time-dependent accessibility metrics (Fransen et al. 2015; Owen and Levinson 2015; Farber et al. 2016). One problem with travel time research based on GTFS schedules is that this format implicitly ignores inaccuracies in travel times due to, among other causes, operational delays, service interruptions or unrealistic schedules. Researchers requiring more accurate measures of transit travel times have relied on field measurements and large-scale simulation models of multimodal network assignments. But these sources of data are expensive to collect, may take years to implement, may not be very accurate, and are difficult to implement on a large network, in continuous time, and in perpetuity. To account for these shortcomings, we put forward a new methodology that capitalizes on freely available vehicle location data feeds to produce a routable retrospective transit network using open source tools. This network contains actual travel times that are true to observed transit network performance, not the expected performance contained in the official transit schedules. In this paper we will describe the toolchain we developed and demonstrate its use in detecting service that differs from the schedule, as well as the implications of these differences for end-to-end travel times and time-dependent accessibility scores. 2. Data and Methods This research uses two primary datasets, a current GTFS package for the Toronto Transit Commission (TTC) and a 48-hour extract of all vehicle locations reported by the TTC-NextBus

The Performance Analysis of the Map-Aided Fuzzy Decision Tree Based on the Pedestrian Dead Reckoning Algorithm in an Indoor Environment.

Hardware sensors embedded in a smartphone allow the device to become an excellent mobile navigator. A smartphone is ideal for this task because its great international popularity has led to increased phone power and since most of the necessary infrastructure is already in place. However, using a smartphone for indoor pedestrian navigation can be problematic due to the low accuracy of sensors, imprecise predictability of pedestrian motion, and inaccessibility of the Global Navigation Satellite System (GNSS) in some indoor environments. Pedestrian Dead Reckoning (PDR) is one of the most common technologies used for pedestrian navigation, but in its present form, various errors tend to accumulate. This study introduces a fuzzy decision tree (FDT) aided by map information to improve the accuracy and stability of PDR with less dependency on infrastructure. First, the map is quickly surveyed by the Indoor Mobile Mapping System (IMMS). Next, Bluetooth beacons are implemented to enable the initializing of any position. Finally, map-aided FDT can estimate navigation solutions in real time. The experiments were conducted in different fields using a variety of smartphones and users in order to verify stability. The contrast PDR system demonstrates low stability for each case without pre-calibration and post-processing, but the proposed low-complexity FDT algorithm shows good stability and accuracy under the same conditions.

Diagnosing service quality using customer reviews: an index approach based on sentiment and gap analyses

Recognizing the importance and potential benefits of customer reviews as a source of the voice of customers, this study proposes an analytic framework and procedures for analyzing customer reviews—termed a customer review-based gap analysis—that are tailored to diagnosing service quality. To this end, we conduct sentiment analysis on customer reviews to capture customers’ perceptions and expectations at the service-feature level, which are not expressed explicitly in their reviews. A case study of a mobile navigation service shows that the customer review-based gap analysis can provide the practical information required to diagnose service quality from customer review data. The suggested indexes for capturing customers’ perceptions and expectations reveal quality strengths and drawbacks at the service-feature level. In addition, incorporating these indexes into those for quality performance and objectives based on a service-feature hierarchy provides a diagnostic tool capable of examining service quality in both overall as well as detailed aspects.

Activity Recognition Using Fusion of Low-Cost Sensors on a Smartphone for Mobile Navigation Application

Low-cost inertial and motion sensors embedded on smartphones have provided a new platform for dynamic activity pattern inference. In this research, a comparison has been conducted on different sensor data, feature spaces and feature selection methods to increase the efficiency and reduce the computation cost of activity recognition on the smartphones. We evaluated a variety of feature spaces and a number of classification algorithms from the area of Machine Learning, including Naive Bayes, Decision Trees, Artificial Neural Networks and Support Vector Machine classifiers. A smartphone app that performs activity recognition is being developed to collect data and send them to a server for activity recognition. Using extensive experiments, the performance of various feature spaces has been evaluated. The results showed that the Bayesian Network classifier yields recognition accuracy of 96.21% using four features while requiring fewer computations.

A participatory web map service: the case of Theewaterskloof Dam

In recent years there have been huge advancements in GIS as a technology and as a science. Previously, GIS was critiqued as a segregating science used exclusively by geospatial experts. In recent years however, various studies have investigated the potential for communities without geospatial training to contribute towards, or benefit from, the use of GIS. Indeed, innovations such as open source and internet based packages like Google Earth and Open Street Map as well as mobile navigation devices have already popularised the importance of geospatial data. This study sought to investigate one way of providing up-to-date maps to fishermen and tourists using dams in South Africa. The paper reports on a pilot case study in the use of web based GIS service for mapping on the Theewaterskloof dam in South Africa. It presents a case study methodology for the development and testing of a web GIS that can be optimised for smartphones and tablets so that communities can access updated information while using the dam, which is rated as dangerous, for fishing or other recreational activity.

Brain signal-based safety measure activation for robotic systems

In this paper, we present our approach for using EEG signals to activate safety measures of a robot when an error or unexpected event is perceived by the human operator. In particular, we consider brain-based error perception while the operator passively observes the robot performing an action. Our approach consists of monitoring EEG signals and detecting a brain potential called error related negativity (ERN) that spontaneously occurs when the operator perceives an error made by the robot or when an unexpected event occurs. We detect ERN by pre-training two linear classifiers using data collected from a preliminary experiment based on a visual reaction task. We derive the probability of failure in demand (PFD), commonly used to assess functional safety for a two-channel verification system based on the combination of linear classifiers. Functional safety analysis was then performed on a BMI-based robotic framework in which a signal was sent to the robot to active its safety measures in when an ERN was detected. Using brain-based signals, we demonstrate that it is possible to send an emergency stop action during mobile navigation task when unexpected events occur with an accuracy of 75%.

Location Based Reminder Using Android andGoogle Maps

Android is a new generation of smart mobile phone platform launched by Google. Android provides the support for mobile map and location service. Android is free and open, providing an easy-to-use development kit containing flexible map display and control functions. Google Maps API provides a number of utilities for adding individual content to the Google map and various web map applications can be explored based on Google Maps API. The application for GPS expands the use of GPS devices. It has a simple design and intuitive operation and environment. The basic characteristic of the application is alerting the user regarding the reminder that is set with the event and the location of the event, when the user is within the range of the destination location and to display a route between the present user location and the set location. The big advantage is an on-line access to the application. The results prove that the solution of mobile navigation system based on Google Maps is feasible and valid, and it can be installed in PDA, mobile phones and other portable devices which run on Android platform

A context-aware pedestrian navigation system

Today's internet and wireless networks have been improved and it has been possible to use various sensors like GPS and Accelerometer in mobile devices. Also considering other improvements of mobile devices, these devices have being changed from toys into tools. Mobile navigation services are those of the applications that help users in navigation process through determination their current location. Being of users in different contexts influences the way of presenting services, since different contexts need different navigation. In this paper, pedestrian navigation services have been classified into five categories: Location Finding, Optimal Path Finding, Orientation, Positioning and Way-finding. Regarding analyses and surveys, all current navigation services can be one of these categories. Also by specifying of effective contexts in pedestrian navigation we considered four abstract classes: Mobile, Mobile Infrastructure, Movement and Motivation. Each of these classes has its own subclasses. The subclasses have their own attributes and methods that in action demonstrate effective contexts and some procedures of using them in pedestrian navigation systems. Considering some of usable and sensible contexts, we implemented a context-aware outdoor pedestrian navigation system. Our empirical implementation and its surveys corroborate the theoretical model for the context-aware map presentation in a pedestrian navigation system.

Telerobotic Haptic Exploration in Art Galleries and Museums for Individuals with Visual Impairments.

This paper presents a haptic telepresence system that enables visually impaired users to explore locations with rich visual observation such as art galleries and museums by using a telepresence robot, a RGB-D sensor (color and depth camera), and a haptic interface. The recent improvement on RGB-D sensors has enabled real-time access to 3D spatial information in the form of point clouds. However, the real-time representation of this data in the form of tangible haptic experience has not been challenged enough, especially in the case of telepresence for individuals with visual impairments. Thus, the proposed system addresses the real-time haptic exploration of remote 3D information through video encoding and real-time 3D haptic rendering of the remote real-world environment. This paper investigates two scenarios in haptic telepresence, i.e., mobile navigation and object exploration in a remote environment. Participants with and without visual impairments participated in our experiments based on the two scenarios, and the system performance was validated. In conclusion, the proposed framework provides a new methodology of haptic telepresence for individuals with visual impairments by providing an enhanced interactive experience where they can remotely access public places (art galleries and museums) with the aid of haptic modality and robotic telepresence.

Indoor Slope and Edge Detection by Using Two-Dimensional EKF-SLAM with Orthogonal Assumption

In an indoor environment, slope and edge detection is an important problem in simultaneous localization and mapping (SLAM), which is a basic requirement for mobile robot autonomous navigation. Slop...

Which one helps tourists most? Perspectives of international tourists using different navigation aids

This study aims to examine wayfinding efficiency and behaviors of international tourists when using a mobile navigation aid, as compared with a paper map and local directional signs. Moreover, it intends to explore the extent to which spatial knowledge acquisition varies among those three groups. A total of 86 subjects participated in this study, and completed two wayfinding tasks, starting from a Mass Rapid Transit train station and arriving at two attraction sites, by using assigned wayfinding navigation aids. The results showed significant differences in wayfinding efficiency and level of feeling lost among participants using different navigation aids. The participants who used an English paper map spent the shortest time in wayfinding for both tasks. The participants who used a 3G-based mobile navigation aid gave the highest score to ‘feeling lost all the time,’ as compared with the two other groups. However, the participants using different navigation aids exhibited no significant differences in regard to their wayfinding anxiety and difficulties. According to the sketch maps drawn by the participants, those who used the local signs for wayfinding provided more detailed information/landmarks, and their walking routes were more accurate than those of the two other groups.

A knowledge-based framework for a mobile OLAP system

We propose in this paper a knowledge-based framework for a mobile OLAP. Its main goal is to allow decision-makers to, efficiently, access datasets in OLAP system anywhere and anytime. The challenge of this work is to be able to improve decisional performances while overcoming capability context. To achieve this goal, the framework integrates in a systematic, generic and extensible way, knowledge in the context-aware recommender process, on one hand. On the other hand, it proposes contextual recommendations. For that purpose, the context-aware recommender system exploits the knowledge extracted (profile in a particular situation ‘contextual profile’) automatically and the current user’s contextual profile to compute a list of contextual recommendations (analysis) adapted to the capability context. We conducted a set of experiments to evaluate the performance of our knowledge-based framework. The results are encouraging and show that our framework contributes significantly to improve mobile OLAP navigation.

Dynamic Navigation Method with Multisubstations Based on Doppler Shift

The mobile terminals must be compensated for the Doppler effect in their moving communication. This special characteristic of mobile communication can be developed in some new applications. This paper proposes methods to realize mobile navigation calculation via Doppler shifts. It gives the theory of relationship between the motion parameters, like directions and speed, and frequency shifts caused by multibase stations. The simulation illustrates how to compute the movement parameters of numerical calculation and what should be care for the problem near angle 90 degree. It also gives an application with Google map and dynamical locating position and direction on a mobile phone by public wireless network. Given the simulation analysis and navigation test, the results show that this method has a good effect.

Исследование некоторых алгоритмов одномаяковой мобильной навигации АНПА: результаты моделирования и морских испытаний

Исследование некоторых алгоритмов одномаяковой мобильной навигации АНПА: результаты моделирования и морских испытаний