Integration of Low-Cost GNSS and IMU Data: A Case Study

Abstract

Today's technological advancements highlight the critical role of positioning systems in engineering applications and interdisciplinary fields. Light Detection and Ranging (LiDAR), Inertial Measurement Unit (IMU), and Global Navigation Satellite System (GNSS)-based systems are widely used in areas such as geomatics engineering, smart city applications, unmanned aerial vehicles, autonomous vehicles, and robotic systems. However, high accuracy requirements and budget constraints make the use of low-cost systems challenging. In this context, the integration of GNSS and IMU data provides a cost-effective solution while contributing to the improvement of positioning accuracy. This study examines the creation of a route map using the integration of GNSS and IMU data obtained from an IPhone 13 Pro device during a walking route of approximately 330 meters in Istanbul's Bebek district. In the MATLAB environment, data were first processed using Butterworth and Kalman filtering techniques, with weights assigned as 95% to GNSS data and 5% to IMU data, minimizing deviations. The parameters of the Kalman filter were optimized to achieve more accurate results. Subsequently, a different scenario was applied by increasing the GNSS weight to 98% and decreasing the IMU weight to 2%, thereby enhancing the accuracy of the integrated data. This process optimized the high accuracy of GNSS data and the contribution of IMU data during signal loss situations. The study demonstrates that high-accuracy positioning can be achieved by processing GNSS and IMU data obtained from low-cost devices with appropriate algorithms. Moreover, this study contributes to the literature by being the first to combine GNSS and IMU data obtained from the IPhone 13 Pro. Additionally, it introduces innovations by integrating the two datasets without any external antenna or post-processing applied to enhance the accuracy of the IPhone 13 Pro GNSS data. This method offers cost-effective solutions for low-budget projects, providing significant contributions to engineering applications.

Keywords

• GNSS
• IMU
• Butterworth Filter
• Kalman Filter
• IPhone 13 Pro
• Low-Cost Systems

Düşük Maliyetli GNSS ve IMU Verilerinin Entegrasyonu: Uygulama Örneği

Abstract

Günümüz teknolojik gelişmeleri, konum belirleme sistemlerinin mühendislik uygulamaları ve disiplinler arası alanlardaki kritik rolünü vurgulamaktadır. Light Detection and Ranging (LiDAR), Inertial Measurement Unit (IMU) ve Global Navigation Satellite System (GNSS) tabanlı sistemler, geomatik mühendisliği, akıllı şehir uygulamaları, insansız hava araçları, otonom araçlar ve robotik sistemler gibi alanlarda yaygın olarak kullanılmaktadır. Ancak, yüksek hassasiyet gereksinimleri ve bütçe kısıtları, düşük maliyetli sistemlerin yeterli doğrulukla veri sağlayamaması nedeniyle kullanımını zorlaştırmaktadır. Bu bağlamda, GNSS ve IMU verilerinin entegrasyonu, maliyet etkin bir çözüm sunarken doğruluğun artırılmasına katkı sağlamaktadır. Bu çalışma, IPhone 13 Pro cihazından elde edilen GNSS ve IMU verilerinin entegrasyonu ile İstanbul’un Bebek semtinde yaklaşık 330 metrelik bir yürüyüş rotası boyunca bir yol haritası oluşturulmasını incelemiştir. MATLAB ortamında gerçekleştirilen analizde, önce Butterworth ve Kalman filtreleme teknikleri uygulanarak veriler işlenmiş, GNSS verilerine %95, IMU verilerine %5 ağırlık verilerek sapmalar minimize edilmiştir. Kalman filtresinin parametreleri optimize edilerek daha doğru sonuçlar elde edilmiştir. Daha sonra, GNSS ağırlığı %98’e çıkarılıp IMU ağırlığı %2’ye düşürülerek farklı bir senaryo uygulanmış, böylece entegre verilerin doğruluğu artırılmıştır. Bu süreçte, GNSS verilerinin yüksek hassasiyeti ile IMU’nun sinyal kaybı durumlarındaki katkısı optimize edilmiştir. Çalışma, düşük maliyetli cihazlardan elde edilen GNSS ve IMU verilerinin uygun algoritmalarla işlenmesiyle yüksek doğrulukta konum belirleme sağlanabileceğini göstermektedir. Ayrıca, bu çalışma IPhone 13 Pro ile elde edilen GNSS ve IMU verilerinin birleştirilmesi açısından ilk defa uygulanmasının yanı sıra, IPhone 13 Pro GNSS verilerine herhangi bir ek anten ya da post-process işlemi ile doğruluk artırma çalışması yapılmadan iki verinin birleştirilmesi açısından literatüre yenilikler katmaktadır. Bu yöntem, düşük bütçeli projeler için maliyet etkin çözümler sunarak mühendislik projelerine önemli bir katkı sağlamaktadır.

Keywords

• GNSS
• IMU
• Butterworth Filtresi
• Kalman Filtresi
• iPhone 13 Pro
• Düşük Maliyetli Sistemler

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