GPS Receiver Position Augmentation Using Correntropy Kalman Filter in Low Latitude Terrain

If any Global Positioning System (GPS) receiver is operated in low latitude regions or urban canyons, the visibility further reduces. These system constraints lead to many challenges in providing precise GPS position accuracy over the Indian subcontinent. As a result, the standalone GPS accuracy does not meet the aircraft landing requirements, such as Category I (CAT-I) Precision Approaches. However, the required accuracy can be achieved by augmenting the GPS. Among all these issues, the predominant factors that significantly influence the receiver position accuracy are selecting a user/receiver position estimation algorithm. In this article, a novel method is proposed based on correntropy and designated as Correntropy Kalman Filter (CKF) for precise GPS applications and GPS Aided Geosynchronous equatorial orbit Augmented Navigation (GAGAN) based aircraft landings over the low latitude Indian subcontinent. The real-world GPS data collected from a dual-frequency GPS receiver located in the southern region of the Indian subcontinent (IISc), Bangalore with Lat/Long: 13.021°N/ 77.5°E) is used for the performance evaluation of the proposed algorithm. Results prove that the proposed CKF algorithm exhibits significant improvement (up to 34%) in position estimation compared to the traditional Kalman Filter.

Augmented Reality Application using Dynamic Location-Based Tracking of Taman Ayun Temple

Taman Ayun Temple is a world cultural heritage in Bali. Based on observations, information regarding the location at Taman Ayun Temple is still not optimal. This study aims to design an application that displays location information using markerless augmented reality. Markerless AR is a technology that displays virtual objects into the real world using GPS, digital compass, and accelerometer. The application is designed using the Wikitude SDK platform and displays information on location, description, image, distance from the user, and location direction. Data is stored in a database server and managed using the web server. Applications are in Indonesian and English. The testing compares the actual distance with the distance displayed in the application using devices with different OS and RAM. Application speed is less than 1 second depending on RAM and internet speed, while location accuracy depends on smartphone GPS accuracy with a difference of less than 10 meters from the actual distance.

LOCATION BASED FIRE DETECTION, WITH NEAREST FIRE FIGHTER FINDER

The lack of fire department makes it hard for most people to contact where the nearest fire department to the scene. Furethermore, when the fire department location was founded it is still a challenge for the fire fighters to find exact location of the fire. For this reason, we used sensitive sensors of fire to the flame. The project used arduino uno microcontrollers, fire sensors, Neo-7m Ublox GPS, ESP8266, and wavecom. As soon as the flame sensor detects the flame, ESP8266 will send the gps data from the home location to the web server www.thingspeak.com which then data from thingspeak is taken to do the nearest distance search using the method of Haversine Formula then send the sms using wavecom sms gammu gameway to the firefighter the nearest fire. The final result of this project shows that the flame sensor can detect a flame of about 75 cm, the GPS accuracy level approaches 100%, ESP8266 can transmit data to Thingspeak by about 20 seconds, accuracy method of accurate formula accuracy calculates Distance near 100% SMS Gateway Wavecom sends sms less than 10 seconds.

BIM AND VR/AR TECHNOLOGIES: FROM PROJECT DEVELOPMENT TO LIFECYCLE ASSET MANAGEMENT

Building Information Modeling (BIM) is becoming more and more an industrial standard for the AEC industry that can provide an integrated approach for effectively managing data and information throughout a project lifecycle. Planning, design, and construction phases of different types of projects can benefit from integrating other technologies such as Virtual Reality (VR) and Augmented Reality (AR) with BIM to enhance its functionalities, including virtual walkthrough, schedule visualization, clash detection, and as-built modeling. Additionally, asset management functions during the maintenance and operation phase of facilities and infrastructure can substantially benefit from this kind of integration. This paper will provide a comprehensive review upon several emerging technologies regarding (1) technological advancements reflected by software and hardware development, (2) applications in the design, construction and asset management through integration with BIM in all phases of projects, and (3) current state of research and practice. It will specifically, provide some limiting factors that prevent VR/AR from further deployment, including lack of GPS accuracy, complexity in development and unclear data security will be explained. Additionally, this paper will present the ben

Augmenting GPS with Geolocated Fiducials to Improve Accuracy for Mobile Robot Applications

In recent decades Global Positioning Systems (GPS) have become a ubiquitous tool to support navigation. Traditional GPS has an error in the order of 10–15 m, which is adequate for many applications (e.g., vehicle navigation) but for many robotics applications lacks required accuracy. In this paper we describe a technique, FAGPS (Fiducial Augmented Global Positioning System) to periodically use fiducial markers to lower the GPS drift, and hence for a small time-period have a more accurate GPS determination. We describe results from simulations and from field testing in open-sky environments where horizontal GPS accuracy was improved from a twice the distance root mean square (2DRMS) error of 5.5 m to 2.99 m for a period of up-to 30 min.

Deep Learning-Based Landmark Detection for Mobile Robot Outdoor Localization

Outdoor mobile robot applications generally implement Global Positioning Systems (GPS) for localization tasks. However, GPS accuracy in outdoor localization has less accuracy in different environmental conditions. This paper presents two outdoor localization methods based on deep learning and landmark detection. The first localization method is based on the Faster Regional-Convolutional Neural Network (Faster R-CNN) landmark detection in the captured image. Then, a feedforward neural network (FFNN) is trained to determine robot location coordinates and compass orientation from detected landmarks. The second localization employs a single convolutional neural network (CNN) to determine location and compass orientation from the whole image. The dataset consists of images, geolocation data and labeled bounding boxes to train and test two proposed localization methods. Results are illustrated with absolute errors from the comparisons between localization results and reference geolocation data in the dataset. The experimental results pointed both presented localization methods to be promising alternatives to GPS for outdoor localization.

Low Cost and Centimeter-Level Global Positioning System Accuracy Using Real-Time Kinematic Library and Real-Time Kinematic GPSA

Accuracy and total design and implementation cost of the GPS framework determine the viability of GPS based projects. As the greater part of the advanced framework including telemetry, IoT, Cloud, and AUTOSAR frameworks use GPS to get exact outcomes, finding a software-controlled error correction becomes important. With the execution of open source library such as RTKLIB will help in controlling and revising GPS blunders. The project utilizes the RTKLIB along with two stations for better accuracy. The RTK-GPS framework works under Linux environment, which is embedded in the Beagleboard. The communication between the GPS system is set up utilizing both serial communication protocol and TCP/IP suite. To get high precision inside the network, two GPS modules are utilized. One of them will be mounted on the rover and another GPS is the base station of the setup. Both the GPS will have a double radio wire setup to increase the reception level to reduce the noise and get centimeter-level precision. For long-range communication, Rover utilizes Wi-Fi with TCP/IP stack protocol. In this research paper, setup is intended to accomplish the centimeter level precision through libraries in a Linux environment. The design will be set up and tried on a college campus under various conditions with different error parameters to acquire a low cost and centimeter level GPS accuracy.