Navigation Applications Research Articles

Metric Basis of Four-Dimensional Klein Bottle

The Metric of a graph plays an essential role in the arrangement of different dimensional structures and finding their basis in various terms. The metric dimension of a graph is the selection of the minimum possible number of vertices so that each vertex of the graph is distinctively defined by its vector of distances to the set of selected vertices. This set of selected vertices is known as the metric basis of a graph. In applied mathematics or computer science, the topic of metric basis is considered as locating number or locating set, and it has applications in robot navigation and finding a beacon set of a computer network. Due to the vast applications of this concept in computer science, optimization problems, and also in chemistry enormous research has been conducted. To extend this research to a four-dimensional structure, we studied the metric basis of the Klein bottle and proved that the Klein bottle has a constant metric dimension for the variation of all its parameters. Although the metric basis is variying in 3 and 4 values when the values of its parameter change, it remains constant and unchanged concerning its order or number of vertices. The methodology of determining the metric basis or locating set is based on the distances of a graph. Therefore, we proved the main theorems in distance forms.

Optical system design method of the all-day starlight refraction navigation system

The application of starlight refraction navigation to spacecraft and space weapons is a significant development direction. Observing enough refracted stars for the star sensor in a strong limb background is an urgent problem. The all-day optical system parameters are analyzed based on the star detection requirement and navigation accuracy. Combined with primary aberration theory, the prime-focus catadioptric optical system is selected to meet the design requirements of a wide field of view (FOV) and tight structure. An H-band (1.52 μm–1.78 μm) star sensor is designed with an FOV of 6°, a focal length of 831 mm, an effective aperture of 253 mm, and a relative distortion of 0.03%. The energy concentration of the star point is 85% within 30 μm, and the maximum lateral chromatic aberration is 2.9 μm, which meets the imaging requirements. Furthermore, a baffle is designed to avoid the influence of direct sunlight on stellar imaging. The proposed method can provide a theoretical foundation and technical support for the optical design of the refraction star navigation.

Application of intraoperative navigation in endoscopic endonasal surgery of paranasal mucocele

Application of intraoperative navigation in endoscopic endonasal surgery of paranasal mucocele

Analysis of CYGNSS Soil Moisture Retrieval Based on ANN over a Selected Region in Ethiopia

Soil moisture (SM) plays a vital role in agriculture, ecosystem functioning, water conservation, weather predictions and climate models. High spatial and temporal frequency data of soil moisture is crucial for agricultural and other important applications. Recent advancements have brought attention to the possibility of using GNSS reflectometry (GNSS-R) for applications on land such as snow sensing, soil moisture retrieval, sea surface monitoring and other applications in addition to positioning, navigation, and timing applications of GNSS. Cyclone Global Navigation Satellite System (CYGNSS) is designed to improve hurricane forecasting by studying the interaction between the ocean and the atmosphere within tropical cyclones. However recent studies show the opportunity of this system for high spatio-temporal soil moisture retrieval. This study presents a machine learning-based approach to get SM at a selected region in Ethiopia using CYGNSS data and analysis of the result. Artificial Neural Network (ANN) model is developed and used to predict soil moisture. The Soil Moisture Active Passive (SMAP) global soil moisture data have been used as reference data in the ML algorithm. The proposed approach has achieved a good correlation between predicted values of soil moisture and reference values from SMAP.

A Survey on Outdoor Navigation Applications for People With Visual Impairments

Outdoor navigation remains a challenging activity for People with Visual Impairments (PVI). Having examined the current literature, we conclude that there are very few publications providing a nuanced understanding of how PVI undertake a journey in an outdoor environment and what are their main challenges and obstacles. This is a critical step towards developing robust solutions that meet the requirements of this user group. We undertook a questionnaire-based study with the National Council for the Blind of Ireland (NCBI) and 49 PVI. According to the feedback from our questionnaire, current journey navigation apps do not provide PVI with sufficient information about traffic lights, crossroads, and physical obstacles to support a satisfactory interaction. Our study reveals key aspects of how PVI interact with outdoor navigation applications. Critical gaps exist, for example, over 63% of respondents indicated they had suffered an injury on at least one previous occasion when navigating outdoors. Based on the questionnaire feedback, we present a solution covering the main aspects of outdoor navigation for PVI. Our work aims to contribute to the improvement of outdoor navigation applications for PVI in the future.

Augmented reality navigation application to promote tourism to local state attraction “Lawang Sewu”

The writing of this paper and the creation of this application is a means to improve and add on facilities on the local state-run attraction. This subject matter was picked in conjunction with the decline of tourism in the said historical site due to a pandemic that rankles Indonesia's tourism industry and drives it to the ground. Augmented Reality as a method of deliverance was picked due to its popularity and freshness in the tourism market. The research method was conducted starting with analysis, development, implementation, then evaluation. Analysis was done using sourcing available to the public and a questioner. Implementation was done using Unity, Vuforia, and Maya. And with the positive responses of 66.6 percent of the sample market finding enjoyment in the use of this application in their exploration during User Acceptance testing with the existing prototype. This shows that the resulting use of this application improves user enjoyment in experiencing the state attraction.

Comparing the accuracy of estimated time of arrival in mobile navigation applications

Comparing the accuracy of estimated time of arrival in mobile navigation applications

Comparing the accuracy of estimated time of arrival in mobile navigation applications

Accuracy of estimated time of arrival (ETA) for mobile vehicle tracking applications (apps) remains as a key issue to be discussed. Most of the mobile vehicle tracking apps such as WAZE is using GPS to track the current location of the vehicles. However, the accuracy of data provided by the mobile apps and factors affecting the accuracy remain as an unexplored area. This study examines the ETA accuracy by comparing ETA of a self-developed vehicle tracking app namely UCSITravel and a popular navigation app namely WAZE. The comparison test is performed by using t-test to determine if there is a statistically significant difference between the means of the datasets. Factors affecting ETA accuracy are determined in this study. The comparison results show that UCSITravel, which uses mean-value calculation (MAP), produces as accurate ETA as WAZE, which calculates ETA based on historic and real-time crowdsourced traffic data. The results shed light to mobile application developers to explore and understand more options of prediction models in developing mobile vehicle tracking app. In addition, this study has demonstrated several factors that were found to affect the accuracy of ETA in both apps as in line with literature. The results yield theoretical and practical implications.

Design and Development of a User-Centered Mobile Application for Intermodal Public Transit in Bangkok: A Design Thinking Approach

With the constant increase in public transit coverage in Bangkok Metropolitan Area in Thailand, many people are still hesitant to switch from using private to public transit, with one potential cause being the unavailability and difficulty in accessing accurate and timely information about their itineraries, while they are commuting. To assess and tackle such issues, the researchers adopted the user-centric Design Thinking methodology to empathize with target users' pain points in this study. They proposed a solution in a user-centric manner by assessing the usability flaws of existing mobile navigation applications, such as Google Maps and ViaBus. By developing a holistic mobile application called 'Disgovery' that covers all modes of public transit in the Bangkok Metropolitan Area and provides relevant information about their trips, the researchers aim to help commuters in Bangkok easily access it in a timely fashion. Through the user-friendly interface, commuters can eliminate the difficulty of finding routes and prices suitable to their needs. By making public transit more accessible with the help of ubiquitous mobile computing, commuters are also encouraged to switch from using private vehicles to public transit, which also can reduce accidents and carbon emissions. The findings from the usability testing in this study suggest that 'Disgovery' is an effective and user-friendly application for daily commuters in Bangkok that can help them achieve their goals without difficulties. The findings also indicate the importance of user interface and user experience guidelines in designing such applications.

THE SOLUTIONS TO ORGANIZE DATA FOR TRAFFIC INFRASTRUCTURE MANAGMENT COMBINED

In the 4.0 revolution, almost every problem is gradually being solved by technology; the pillars are still Cloud Computing, Big Data, AI, and IoT. Among these problems, the problem of the transport sector is fundamental. Because of its unique characteristics, the analysis and collection of survey data are challenging. The unique feature of the data of each transport infrastructure project is that the data is extensive and has many different formats; the information is updated regularly every year according to the process of conserving, renovating, and renovating. Upgrade if using the traditional way of storing and representing data, like the way database management systems and software are used, will not meet the demand. Therefore, it is necessary to implement a solution to build specialized databases, with a special organizational plan according to industry characteristics and the ability to integrate data from specialized applications to have linkage and unity data between authorities. This will be a data platform for exploitation and sharing for professional management work between authorities in the city and the whole country. The planning and maintenance of transportation infrastructure is a practical application. However, there is now interest in a new area of ​​navigation applications in maritime transport and electronic charts. This feature requires GIS support. The transportation system is an essential factor serving the travel needs of the people, helping the production process to take place continuously and operate normally. In our country, transportation is more and more focused. Our country has been building an increasingly complete and developed transportation system; transport infrastructure is considered essential in promoting our country’s economic, cultural and social activities. This paper proposes a solution to organize traffic infrastructure management data in combination with digital maps. Application to edit data of transportation infrastructure information for Hai Chau and Son Tra districts. At the same time, we have also integrated most of the famous map platforms in the world, such as Google Map, BingMap, OpenStreetMap, HereMap, and IOTLink

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.

An Intelligent Multi-Floor Navigational System Based on Speech, Facial Recognition and Voice Broadcasting Using Internet of Things.

Modern technologies such as the Internet of Things (IoT) and physical systems used as navigation systems play an important role in locating a specific location in an unfamiliar environment. Due to recent technological developments, users can now incorporate these systems into mobile devices, which has a positive impact on the acceptance of navigational systems and the number of users who use them. The system that is used to find a specific location within a building is known as an indoor navigation system. In this study, we present a novel approach to adaptable and changeable multistory navigation systems that can be implemented in different environments such as libraries, grocery stores, shopping malls, and official buildings using facial and speech recognition with the help of voice broadcasting. We chose a library building for the experiment to help registered users find a specific book on different building floors. In the proposed system, to help the users, robots are placed on each floor of the building, communicating with each other, and with the person who needs navigational help. The proposed system uses an Android platform that consists of two separate applications: one for administration to add or remove settings and data, which in turn builds an environment map, while the second application is deployed on robots that interact with the users. The developed system was tested using two methods, namely system evaluation, and user evaluation. The evaluation of the system is based on the results of voice and face recognition by the user, and the model's performance relies on accuracy values obtained by testing out various values for the neural network parameters. The evaluation method adopted by the proposed system achieved an accuracy of 97.92% and 97.88% for both of the tasks. The user evaluation method using the developed Android applications was tested on multi-story libraries, and the results were obtained by gathering responses from users who interacted with the applications for navigation, such as to find a specific book. Almost all the users find it useful to have robots placed on each floor of the building for giving specific directions with automatic recognition and recall of what a person is searching for. The evaluation results show that the proposed system can be implemented in different environments, which shows its effectiveness.

Time-differenced double difference method for measurement of Navigation with Indian Constellation (NavIC) receiver differential phase bias

The pseudo-range (PR) and carrier phase (CP) observations of Global Navigation Satellite System (GNSS)/Navigation with Indian Constellation (NavIC) receivers are influenced due to various errors like ionosphere delay, troposphere delay, receiver and satellite clock error, multipath, integer ambiguity, receiver hardware noise etc. However, the real-time positioning and navigation applications are in quest of high-quality carrier phase measurements with zero bias. The GNSS/NavIC receiver hardware noise is one of the critical error sources that degrade the quality of carrier phase measurements. And it can be measured by computing the differential phase bias or carrier phase bias employing the double-difference method. This research article proposes a novel approach called Time Differenced Double Difference Carrier Phase (TDDDCP) for estimating differential phase bias or carrier phase bias of NavIC receiver. Moreover, the proposed TDDDCP technique has the advantage over the Double Difference Carrier Phase (DDCP) because it requires data from only one satellite with two zero base length receivers for which the differential phase bias is to be measured. The experimental results show that the differential phase bias value differs for different satellites, ranging from −0.3904 m to 0.3168 m. The bias-free carrier phase observations based on position coordinates (X, Y, Z) found improved precision of standard deviation of 0.4176 m, 0.645 m, and 0.328 m, respectively.

Improved decentralized GNSS/SINS/odometer fusion system for land vehicle navigation applications

Due to low cost and complementary performance advantages, global navigation satellite system (GNSS)/ strapdown inertial navigation system (SINS) integrated systems have established themselves in certain areas of land vehicle navigation. However, this integrated system cannot maintain reliable positioning solutions in challenging environments due to the inherent fragility of GNSS signals and time accumulated errors of a stand-alone SINS. To address this challenge, a multi-source information fusion system based on the decentralized system architecture and sequential Kalman filter for a land vehicle is proposed, which can fuse information from an odometer and motion aided constraints selectively and adaptively in different driving environments. Moreover, a comprehensive calibration and compensation strategy is designed to enhance the information fusion. On the one hand, a real-time calibration algorithm is designed to estimate the time-varying odometer scale factor and the misalignment between the inertial measurement unit (IMU) and vehicle body frame when GNSS signals are available. On the other hand, the forward velocity error caused by the lever arm, and the non-zero lateral velocity generated by the turning maneuver are compensated by the introduced velocity compensation method. A real car experiment in urban areas is carried out to illustrate the effectiveness of the proposed system. It shows that the proposed decentralized GNSS/SINS/odometer fusion system can maintain an average horizontal positioning root mean square error (RMSE) of 1-meter level when GNSS signals are cut off about 1-2 min. In addition, compared with the traditional centralized fusion structure, the proposed decentralized fusion structure can mitigate the horizontal positioning RMSE of the whole trajectory from 2.95 m to 0.59 m, which verifies that it can obtain better performance for the application of low-cost sensors in complex GNSS environments.

What makes the unsupervised monocular depth estimation (UMDE) model training better

Current computer vision tasks based on deep learning require a huge amount of data with annotations for model training or testing, especially in some dense estimation tasks, such as optical flow segmentation and depth estimation. In practice, manual labeling for dense estimation tasks is very difficult or even impossible, and the scenes of the dataset are often restricted to a small range, which dramatically limits the development of the community. To overcome this deficiency, we propose a synthetic dataset generation method to obtain the expandable dataset without burdensome manual workforce. By this method, we construct a dataset called MineNavi containing video footages from first-perspective-view of the aircraft matched with accurate ground truth for depth estimation in aircraft navigation application. We also provide quantitative experiments to prove that pre-training via our MineNavi dataset can improve the performance of depth estimation model and speed up the convergence of the model on real scene data. Since the synthetic dataset has a similar effect to the real-world dataset in the training process of deep model, we finally conduct the experiments on MineNavi with unsupervised monocular depth estimation (UMDE) deep learning models to demonstrate the impact of various factors in our dataset such as lighting conditions and motion mode, aiming to explore what makes this kind of models training better.

A saliency-guided street view image inpainting framework for efficient last-meters wayfinding

Global Positioning Systems (GPS) have played a crucial role in various navigation applications. Nevertheless, localizing the perfect destination within the last few meters remains an important but unresolved problem. Limited by the GPS positioning accuracy, navigation systems always show users a vicinity of a destination, but not its exact location. Street view images (SVI) in maps as an immersive media technology have served as an aid to provide the physical environment for human last-meters wayfinding. However, due to the large diversity of geographic context and acquisition conditions, the captured SVI always contains various distracting objects (e.g., pedestrians and vehicles), which will distract human visual attention from efficiently finding the destination in the last few meters. To address this problem, we highlight the importance of reducing visual distraction in image-based wayfinding by proposing a saliency-guided image inpainting framework. It aims at redirecting human visual attention from distracting objects to destination-related objects for more efficient and accurate wayfinding in the last meters. Specifically, a context-aware distracting object detection method driven by deep salient object detection has been designed to extract distracting objects from three semantic levels in SVI. Then we employ a large-mask inpainting method with fast Fourier convolutions to remove the detected distracting objects. Experimental results with both qualitative and quantitative analysis show that our saliency-guided inpainting method11Codes are available at https://github.com/cbhu523/saliency_last_way_finding/. can not only achieve great perceptual quality in street view images but also redirect the human’s visual attention to focus more on static location-related objects than distracting ones. The human-based evaluation also justified the effectiveness of our method in improving the efficiency of locating the target destination.

Creation of a Mobile Application for Navigation for a Potential Use of People with Visual Impairment Exercising the NTRIP Protocol

The global navigation satellite systems (GNSS) have become important in conjunction with the advancement of technology, in order to improve the accuracy of positioning and navigation on mobile devices. In the current project, a mobile application for navigation using the network transport of restricted test case modeling (RTCM) via internet protocol (NTRIP) was developed, and it has been focused on the autonomous mobility of people with visual disabilities. This occurred through a web viewer that stores the base cartography in a genome database (GDB). Such information is integrated into the application interface with Java Script language within the Android Studio platform, with a personalized design. This incorporates a screen reader for selection, navigation and direction of destinations, in addition to an early warning system for obstacles. Additionally, a differential position correction was implemented using the BKG Ntrip Client (BNC) software, for the adjustment of coordinates with the precise point positioning (PPP) method through streams in the format of RTCM with casters EPEC3, IGS03 and BCEP00BKG0. The evaluation of the application was performed using the National Standard for Spatial Data Accuracy (NSSDA), establishing 30 control points. These were obtained through the fast static method, in order to compare the horizontal accuracy of the observations in static and navigation modes between high-end and mid-range mobile devices.

An indoor service area determination approach for pedestrian navigation path planning

ABSTRACT Indoor navigation has been studied for many years, but it still has many limitations. In current navigation, pedestrians need to tell navigation systems the destination, because it is one of the preconditions for path planning. However, in some indoor cases, pedestrians cannot specify a destination because they have no information about where it is or even cannot be sure if there is a desired one. We believe that determining a service area is a possible way to handle such cases. For example, a service area that can be reached within a 2-min walk. In this paper, we propose an indoor service area determination approach for pedestrian navigation path planning. We demonstrate this approach in a shopping mall with multi-floors. The results show that it can successfully compute the reachable spaces and thereby helping people to select and arrive at the most appropriate destination. This approach is also useful for other indoor navigation applications within public buildings like offices, airports, theaters, hospitals, and museums where pedestrians would like to make a choice between multiple facilities of the same type, such as printers, registration desks, ATMs, AED, garbage bins, even exits.

Different Path Planning Techniques for an Indoor Omni-Wheeled Mobile Robot: Experimental Implementation, Comparison and Optimization

Omni-wheeled mobile robots (Omni WMRs) are commonly used in indoor navigation applications like surveillance, search and rescue, and autonomous transportation. They are always characterized by their versatility, mobility and high payload. This paper presents the mechatronic design, low-level control and high-level control of an indoor 4 Omni-Wheeled Mobile Robot (4OWMR). Since autonomy and path planning are research necessities for WMRs, four heuristic and probabilistic path-planning techniques are chosen for experimental implementation. The selected techniques are PRM (Probabilistic Roadmaps), RRT (Rapidly exploring Random Tree), RRTSTAR (RRT*), and ASTAR (A*) algorithms. The proposed environments are static, expressed by maps with unknown nodes and obstacles. Local path planning is implemented with simultaneous localization and mapping (SLAM). Path planning techniques are programmed, and the obtained paths are optimized by a multi-objective genetic algorithm technique to ensure the shortest path and its smoothness. The optimized paths are deployed to the 4OWMR. The obtained results are compared in terms of travel time, travel distance, average velocity and convergence error. A ranking technique is utilized to rank the obtained results and show the most preferred technique in terms of energy consumption and convergence accuracy in addition to the overall ranking. Experimental results showed that the Hybrid A* algorithm produced the best-generated paths with respect to other techniques.

Real-time route planning of unmanned aerial vehicles based on improved soft actor-critic algorithm.

With the application and development of UAV technology and navigation and positioning technology, higher requirements are put forward for UAV maneuvering obstacle avoidance ability and real-time route planning. In this paper, for the problem of real-time UAV route planning in the unknown environment, we combine the ideas of artificial potential field method to modify the state observation and reward function, which solves the problem of sparse rewards of reinforcement learning algorithm, improves the convergence speed of the algorithm, and improves the generalization of the algorithm by step-by-step training based on the ideas of curriculum learning and transfer learning according to the difficulty of the task. The simulation results show that the improved SAC algorithm has fast convergence speed, good timeliness and strong generalization, and can better complete the UAV route planning task.