An Indoor Navigation System Using a Sensor Fusion Scheme on Android Platform

With the development of wireless communication networks, smart phones have become a necessity for people’s daily lives, and they meet not only the needs of basic functions for users such as sending a message or making a phone call, but also the users’ demands for entertainment, surfing the Internet and socializing. Navigation functions have been commonly utilized, however the navigation function is often based on GPS (Global Positioning System) in outdoor environments, whereas a number of applications need to navigate indoors. This paper presents a system to achieve high accurate indoor navigation based on Android platform. To do this, we design a sensor fusion scheme for our system. We divide the system into three main modules: distance measurement module, orientation detection module and position update module. We use an efficient way to estimate the stride length and use step sensor to count steps in distance measurement module. For orientation detection module, in order to get the optimal result of orientation, we then introduce Kalman filter to de-noise the data collected from different sensors. In the last module, we combine the data from the previous modules and calculate the current location. Results of experiments show that our system works well and has high accuracy in indoor situations

Foot Detection Method for Footwear Augmented Reality Applications

Liitreaalsus on populaarsust koguv platvorm rõivaste ning aksessuaaride kasutamise visualiseerimiseks. Ideaalis võimaldab see kasutajatel proovida erinevaid riideid, jalatseid ja aksessuaare, kasutades ainult üht kaamerat ning sobivat rakendust, mis võimaldab kuvada erinevaid valikuid.\n\rJalatsite liitreaalsuses on palju erinevaid lahendusi, et pakkuda kasutajatele liitreaalsuse kogemust. Need lahendused kasutavad erinevaid meetodeid, nagu fikseeritud kaamera, muutumatu taust ja markerid jalgadel tuvastuse hõlbustamiseks. Nende meetodite hulgas pole ükski kindlalt parem, lihtsam või kiirem. Lisaks puudub tihtipeale avalikkusel ligipääs arendatud rakendustele.\n\rKäesolev magistritöö proovis leida universaalset lahendust, mis sobiks kasutamiseks kõigi tulevaste jalatsite liitreaalsuse rakendustega.Augmented reality is gaining popularity as a technique for visualizing apparel usage. Ide-ally it allows users virtually to try out different clothes, shoes, and accessories, with only a camera and suitable application which encompasses different apparel choices.\n\rFocusing on augmented reality for footwear, there is a multitude of different solutions on how to offer the reality augmentation experience to the end users. These solutions employ different methods to deliver the end result, such as using fixed camera and constant back-ground or requiring markers on feet for detection. Among the variety of techniques used to approach the footwear reality augmentation, there is no single best, simplest, or fastest solution. The solutions’ sources aren’t usually even publicly available. \n\rThis thesis tries to come up with a solution for the footwear reality augmentation problem, which can be used as a base for any proceeding footwear augmented reality projects. This intentionally universal approach will be created by researching possible combinations of potential methods that can ensure a solutions regarding footwear reality augmentation. \n\rIn general, the idea behind this thesis work is to conduct a literature review about different techniques and come up with the best and robust algorithm or combination of methods that can be used for footwear augmented reality.\n\rA researched, documented, implemented and publicized solution would allow any upcom-ing footwear augmented reality related project to start working from an established base, therefore reducing time waste on already solved issues and possibly improving the quality of the end result.\n\rThe solution presented in this thesis is developed with focus on augmented reality applica-tions. The method is neither specific to any platform nor does it have heavy location re-quirements. The result is a foot detection algorithm, capable of working on commonly available hardware, which is beneficial for augmented reality application

Customized Co-Simulation Environment for Autonomous Driving Algorithm Development and Evaluation

Increasing the implemented SAE level of autonomy in road vehicles requires extensive simulations and verifications in a realistic simulation environment before proving ground and public road testing. The level of detail in the simulation environment helps ensure the safety of a real-world implementation and reduces algorithm development cost by allowing developers to complete most of the validation in the simulation environment. Considering sensors like camera, LIDAR, radar, and V2X used in autonomous vehicles, it is essential to create a simulation environment that can provide these sensor simulations as realistically as possible. While sensor simulations are of crucial importance for perception algorithm development, the simulation environment will be incomplete for the simulation of holistic AV operation without being complemented by a realistic vehicle dynamic model and traffic cosimulation. Therefore, this paper investigates existing simulation environments, identifies use case scenarios, and creates a cosimulation environment to satisfy the simulation requirements for autonomous driving function development using the Carla simulator based on the Unreal game engine for the environment, Sumo or Vissim for traffic co-simulation, Carsim or Matlab, Simulink for vehicle dynamics co-simulation and Autoware or the author or user routines for autonomous driving algorithm co-simulation. As a result of this work, a model-based vehicle dynamics simulation with realistic sensor simulation and traffic simulation is presented. A sensor fusion methodology is implemented in the created simulation environment as a use case scenario. The results of this work will be a valuable resource for researchers who need a comprehensive co-simulation environment to develop connected and autonomous driving algorithms

Automated Detection and Counting of Pedestrians on an Urban Roadside

This thesis implements an automated system that counts pedestrians with 85% accuracy. Two approaches have been considered and evaluated in terms of count accuracy, cost and ease of deployment. The first approach employs the Autoscope Solo Terra, a traffic camera which is widely used to monitor vehicular traffic. The Solo Terra supports an image processing-based detector that counts the number of objects crossing user-defined areas in the captured image. The count is updated based on the amount of movement across the selected regions. Therefore, a second approach has been considered that uses a histogram of oriented gradients (HoG), an advanced vision based algorithm proposed by Dalal et al. which distinguishes a pedestrian from a non-pedestrian based on an omega shape formed by the head and shoulders of a human being. The implemented detection software processes video frames that are streamed from a low-cost digital camera. The frames are divided into sub-regions which are scanned for an omega shape whenever movement is detected in those regions. It has been found that the HoG-based approach degrades in performance due to occlusion under dense pedestrian traffic conditions whereas the Solo Terra approach appears to be more robust. Undercounts and overcounts were encountered using the Solo Terra approach. To combat the disadvantages of both the approaches, they were integrated to form a single system where count is incremented predominantly using the Solo Terra. The HoG-based approach corrects the obtained count under certain conditions. A preliminary prototype of the integrated system has been verified

Design and realization of precise indoor localization mechanism for Wi-Fi devices

Despite the abundant literature in the field, there is still the need to find a time-efficient, highly accurate, easy to deploy and robust localization algorithm for real use. The algorithm only involves minimal human intervention. We propose an enhanced Received Signal Strength Indicator (RSSI) based positioning algorithm for Wi-Fi capable devices, called the Dynamic Weighted Evolution for Location Tracking (DWELT). Due to the multiple phenomena affecting the propagation of radio signals, RSSI measurements show fluctuations that hinder the utilization of straightforward positioning mechanisms from widely known propagation loss models. Instead, DWELT uses data processing of raw RSSI values and applies a weighted posterior-probabilistic evolution for quick convergence of localization and tracking. In this paper, we present the first implementation of DWELT, intended for 1D location (applicable to tunnels or corridors), and the first step towards a more generic implementation. Simulations and experiments show an accuracy of 1m in more than 81% of the cases, and less than 2m in the 95%.Peer ReviewedPostprint (published version