AUGMENTED REALITY BASED INDOOR POSITIONING NAVIGATION TOOL.

Nowadays, indoor navigation gained people’s attention. Lots of techniques and technologies have been used in order to develop the indoor navigation. Indoor navigation is far away behind the outdoor navigation. For outdoor navigation, we have GPS to guide and give direction to the desired place. Unfortunately, it is restricted for the outdoor purpose only. Thus, the main objective of this project is to develop an interactive indoor navigation system and augmented reality is being use to superimposed the directional signage. In this project small computer which is Raspberry Pi has been used as a computing device. Probably in the future, all smartphones will have augmented reality based indoor navigation tools because it already equipped with many sensors such as an accelerometer, gyro, and compass which will improve the accuracy of positioning. Basically, the project has been tested at Universiti Teknologi PETRONAS’s Information Resource Centre (IRC), and it has shown its flexibility in working as an indoor positioning tool to navigate to 5 different locations with multiple levels

Indoor Navigation System for the Visually Impaired with User-centric Graph Representation and Vision Detection Assistance

Independent navigation through unfamiliar indoor spaces is beset with barriers for the visually impaired. Hence, this issue impairs their independence, self-respect and self-reliance. In this thesis I will introduce a new indoor navigation system for the blind and visually impaired that is affordable for both the user and the building owners. Outdoor vehicle navigation technical challenges have been solved using location information provided by Global Positioning Systems (GPS) and maps using Geographical Information Systems (GIS). However, GPS and GIS information is not available for indoor environments making indoor navigation, a challenging technical problem. Moreover, the indoor navigation system needs to be developed with the blind user in mind, i.e., special care needs to be given to vision free user interface. In this project, I design and implement an indoor navigation application for the blind and visually impaired that uses RFID technology and Computer Vision for localization and a navigation map generated automatically based on environmental landmarks by simulating a user’s behavior. The focus of the indoor navigation system is no longer only on the indoor environment itself, but the way the blind users can experience it. This project will try this new idea in solving indoor navigation problems for blind and visually impaired users

Deep Network Uncertainty Maps for Indoor Navigation

Most mobile robots for indoor use rely on 2D laser scanners for localization, mapping and navigation. These sensors, however, cannot detect transparent surfaces or measure the full occupancy of complex objects such as tables. Deep Neural Networks have recently been proposed to overcome this limitation by learning to estimate object occupancy. These estimates are nevertheless subject to uncertainty, making the evaluation of their confidence an important issue for these measures to be useful for autonomous navigation and mapping. In this work we approach the problem from two sides. First we discuss uncertainty estimation in deep models, proposing a solution based on a fully convolutional neural network. The proposed architecture is not restricted by the assumption that the uncertainty follows a Gaussian model, as in the case of many popular solutions for deep model uncertainty estimation, such as Monte-Carlo Dropout. We present results showing that uncertainty over obstacle distances is actually better modeled with a Laplace distribution. Then, we propose a novel approach to build maps based on Deep Neural Network uncertainty models. In particular, we present an algorithm to build a map that includes information over obstacle distance estimates while taking into account the level of uncertainty in each estimate. We show how the constructed map can be used to increase global navigation safety by planning trajectories which avoid areas of high uncertainty, enabling higher autonomy for mobile robots in indoor settings.Comment: Accepted for publication in "2019 IEEE-RAS International Conference on Humanoid Robots (Humanoids)

Scalable and Vision Free User Interface Approaches for Indoor Navigation Systems for the Visually Impaired

This thesis introduces scalable and vision free user interface approaches for indoor navigation systems for the visually impaired. Using an Android Smartphone that runs the indoor navigation system – Percept Application with accessibility features, the blind user obtains navigation instructions generated automatically by our navigation generation algorithm to the chosen destination when touching specific landmarks tagged with Near Field Communication tags. This thesis also introduces an Orientation & Mobility Survey Tool that can help O&M Instructors survey the building and deploy such indoor navigation system. The system was deployed and tested in a large building at the University of Massachusetts at Amherst

Indoor Navigation Guidance for Mobile Device

Navigation is capable of showing the position and direction at the specified location. In this paper, we proposed an indoor navigation based on the user location to its desired destination. Indoors navigation first needs to determine the route that can be accessed inside the building. This guidance is made by cultivating the indoor trajectory route that utilizes the sensor technologies in mobile devices. The sensors to be used are pedometer and magnetometer. The experiment shows that the guidance gave the instructions to navigate from the start to the destination by following compass direction and footstep to take. The guidance instruction affected by three factors, there is user height, step calculation, and sensor threshold value. These three factors have different effects but interconnected with the application system and that which causes differences in the accuracy values obtained in the experiment. Keywords : indoor navigation, guidance, sensor, mobile device

iPhone App: Lecturer's Room Finder, UUM-CAS

The Information Technology building at the College of Arts and Sciences, Universiti Utara Malaysia has a complex design. The numbering of the lecturers’ rooms is very confusing and finding the required room is very difficult and taking a long time not just for students but also for lecturers. The purpose of this study is to develop a mobile indoor navigation prototype for lecturers’ room search based on iPhone. This report discusses about indoor navigation and the development of the prototype based on iPhone. This study utilized the research methodology in Information System (IS) which was adapted from Vaishnavi and Kuechler (2004). The use of mobile indoor navigation prototype could help students to find the required room faster while saving energy and increasing productivity. The mobile indoor navigation prototype was developed using web tools such as PHP, HTML5, CSS, JavaScript and SQLite as database. The prototype can be used as an alternative way in finding lecturers’ room at IT building. The results from the evaluation indicated that mobile indoor navigation application for searching lecturers' room at IT building, College of Arts and Sciences, Universiti Utara Malaysia based on iPhone devices has achieved all the objectives. The users agreed in terms of Perceived Usefulness and Perceived Ease of Use towards the use of the prototype