Design of a mobile augmented reality-based indoor navigation system

GPS-based navigation technology has been widely used in most of the commercial navigation applications nowadays. However, its usage in indoor navigation is not as effective as when it is used at outdoor environment. Much research and developments of indoor navigation technology involve additional hardware installation which usually incur high setup cost. In this paper, research and comparisons were done to determine the appropriate techniques of indoor positioning, pathfinding, and route guidance for an indoor navigation method. The aim of this project is to present a simple and cost effective indoor navigation system. The proposed system uses the existing built-in sensors embedded in most of the mobile devices to detect the user location, integrates with AR technology to provide user an immersive navigation experience. In this project, an indoor navigation mobile application was developed and tested. The development demonstrates the usage of Indoor Atlas which enables indoor positioning through technology fusion to detect user’s position and obtain the route to destination, and AR Core to display AR guidance using the computed route. Surveys were carried out to gauge the efficiency of the method and to gather the feedback from the participants. The architecture of the method and the demonstration of the application is presented

Indoor Space Classification Using Cascaded LSTM

Author's accepted manuscript.© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Indoor space classification is an important part of localization that helps in precise location extraction, which has been extensively utilized in industrial and domestic domain. There are various approaches that employ Bluetooth Low Energy (BLE), Wi-Fi, magnetic field, object detection, and Ultra Wide Band (UWB) for indoor space classification purposes. Many of the existing approaches need extensive pre-installed infrastructure, making the cost higher to obtain reasonable accuracy. Therefore, improvements are still required to increase the accuracy with minimum requirements of infrastructure. In this paper, we propose an approach to classify the indoor space using geomagnetic field (GMF) and radio signal strength (RSS) as the identity. The indoor space is an open big test bed divided into different indiscernible subspace. We collect GMF and RSS at each subspace and classify it using cascaded Long Short Term Memory (LSTM). The experimental results show that the accuracy is significantly improved when GMF and RSS are combined to make distinct features. In addition, we compare the performance of the proposed model with the state-of-the-art machine learning methods.acceptedVersio

3D GEOSPATIAL INDOOR NAVIGATION FOR DISASTER RISK REDUCTION AND RESPONSE IN URBAN ENVIRONMENT

Disaster management for urban environments with complex structures requires 3D extensions of indoor applications to support better risk reduction and response strategies. The paper highlights the need for assessment and explores the role of 3D geospatial information and modeling regarding the indoor structure and navigational routes which can be utilized as disaster risk reduction and response strategy. The reviewed models or methods are analysed testing parameters in the context of indoor risk and disaster management. These parameters are level of detail, connection to outdoor, spatial model and network, handling constraints. 3D reconstruction of indoors requires the structural data to be collected in a feasible manner with sufficient details. Defining the indoor space along with obstacles is important for navigation. Readily available technologies embedded in smartphones allow development of mobile applications for data collection, visualization and navigation enabling access by masses at low cost. The paper concludes with recommendations for 3D modeling, navigation and visualization of data using readily available smartphone technologies, drones as well as advanced robotics for Disaster Management

Recent Advances in Indoor Localization Systems and Technologies

Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods

Indoor Positioning and Navigation

In recent years, rapid development in robotics, mobile, and communication technologies has encouraged many studies in the field of localization and navigation in indoor environments. An accurate localization system that can operate in an indoor environment has considerable practical value, because it can be built into autonomous mobile systems or a personal navigation system on a smartphone for guiding people through airports, shopping malls, museums and other public institutions, etc. Such a system would be particularly useful for blind people. Modern smartphones are equipped with numerous sensors (such as inertial sensors, cameras, and barometers) and communication modules (such as WiFi, Bluetooth, NFC, LTE/5G, and UWB capabilities), which enable the implementation of various localization algorithms, namely, visual localization, inertial navigation system, and radio localization. For the mapping of indoor environments and localization of autonomous mobile sysems, LIDAR sensors are also frequently used in addition to smartphone sensors. Visual localization and inertial navigation systems are sensitive to external disturbances; therefore, sensor fusion approaches can be used for the implementation of robust localization algorithms. These have to be optimized in order to be computationally efficient, which is essential for real-time processing and low energy consumption on a smartphone or robot

Self–organised multi agent system for search and rescue operations

Autonomous multi-agent systems perform inadequately in time critical missions, while they tend to explore exhaustively each location of the field in one phase with out selecting the pertinent strategy. This research aims to solve this problem by introducing a hierarchy of exploration strategies. Agents explore an unknown search terrain with complex topology in multiple predefined stages by performing pertinent strategies depending on their previous observations. Exploration inside unknown, cluttered, and confined environments is one of the main challenges for search and rescue robots inside collapsed buildings. In this regard we introduce our novel exploration algorithm for multi–agent system, that is able to perform a fast, fair, and thorough search as well as solving the multi–agent traffic congestion. Our simulations have been performed on different test environments in which the complexity of the search field has been defined by fractal dimension of Brownian movements. The exploration stages are depicted as defined arenas of National Institute of Standard and Technology (NIST). NIST introduced three scenarios of progressive difficulty: yellow, orange, and red. The main concentration of this research is on the red arena with the least structure and most challenging parts to robot nimbleness

Mobile Robots Navigation

Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described

Robust algorithms for the identification and control of Android-powered quadcopters

The focus of this thesis is on modeling and control of a non-real time, Android operated quadcopter of type dji F450 Flame Wheel without having a concrete knowledge about the system's dynamics and parameters. The quadcopter is equipped with an onboard non-rooted Android smartphone, which serves as both the controller and the IMU in the system. The reference command signals are generated by another user-held Android smartphone which defines the desired orientation of the quadcopter. Due to the fact that default Android implementation is not real-time, the measurements of both Android phones are subject to significant latencies resulting in asynchronous data. To obtain a model of the system, a comprehensive system identification study of the quadcopter's rotation dynamics using grey box model and Euler's equations of rigid body is introduced in the thesis. It also introduces two novel algorithms for obtaining an initial guess for the inertia matrix using convex optimization despite the presence of large number of local minimizers in the original prediction error problem. It shows how sensitive the process is to the initial guess of the model's parameters. A detailed comparison of the relevant estimates is also shown. The control laws were implemented on the onboard Android device, which reads the asynchronous built-in sensors measurements and generates the control signals required to steer the quadcopter and obtain the desired orientation defined by the user-held device. Two control laws were developed, an advanced model-free PID controller that accounts for the non-uniformly distributed data, the windup effect, and the derivative kick, and a model-based LQI controller. Both control approaches were able to stabilize the quadcopter despite the data asynchronousity and model uncertainty, and were validated and tested empirically and through simulation. The thesis also introduces a novel approach of optimizing the PID controllers gains based on the jacobian matrix. The optimization problem tends to be poorly conditioned for such systems. Hence, the novel scaling technique improves the conditioning of the optimization problem and obtains better minimizers. The efficiency of the proposed algorithm is evaluated through simulation. Furthermore, a detailed study on the effect of the cost function selection and model uncertainty on the optimization process is shown