Estimasi Posisi Hasil Fusi Sensor IMU-LiDAR Pada Sistem Navigasi Menggunakan Metode Extended Kalman Filter (EKF) Saat GNSS Outage

Husniyyah, Husniyyah (2025) Estimasi Posisi Hasil Fusi Sensor IMU-LiDAR Pada Sistem Navigasi Menggunakan Metode Extended Kalman Filter (EKF) Saat GNSS Outage. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Sistem navigasi kendaraan otonom biasanya menggunakan sensor dengan biaya tinggi seperti GNSS (Global Navigation Satellite System) yang mampu menentukan posisi absolut dengan akurasi tinggi. Namun, akurasi GNSS dapat menurun ketika terjadi hambatan penerimaan sinyal yang disebabkan oleh bias atmosfer dan berbagai kondisi lingkungan seperti gedung tinggi, terowongan, dan pegunungan. Ketika GNSS tidak menerima sinyal satelit, maka sistem mengalami pemadaman (signal outage). Dalam situasi ini, Inertial Measurement Unit (IMU) dan Light Detection and Ranging (LiDAR) dapat digunakan sebagai sensor pendukung. Namun, masing-masing sensor memiliki keterbatasan yaitu IMU cenderung mengalami drift seiring waktu dan odometri LiDAR rentan terhadap kesalahan akumulasi selama pengukuran. Penelitian ini menggunakan metode integrasi antara data odometri LiDAR dan IMU menggunakan Extended Kalman Filter (EKF) untuk estimasi posisi saat GNSS outage. Data dari IMU dan LiDAR dioptimasi melalui Two-step Joint Optimization yang bertujuan untuk meningkatkan akurasi data IMU dan LiDAR pada saat pengukuran. Data yang diperoleh dari proses tersebut diolah menggunakan EKF. Penelitian ini bertujuan untuk mengimplementasikan dan menganalisis kinerja fusi sensor IMU-LiDAR dengan metode EKF, serta meningkatkan akurasi posisi yang dihasilkan saat kondisi GNSS outage. Hasil penelitian menunjukkan bahwa fusi IMU-LiDAR dapat menggantikan GNSS ketika outage dengan simulai EKF yang mempunyai akurasi dengan nilai RMSE 0,0691 m untuk posisi East dan 0,0405 m untuk posisi North.
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Autonomous vehicle navigation systems commonly rely on high-cost sensors such as the Global Navigation Satellite System (GNSS), which can provide highly accurate absolute positioning. However, the accuracy of GNSS may degrade due to signal reception disturbances caused by atmospheric biases and environmental conditions such as tall buildings, tunnels, and mountainous terrain. When GNSS fails to receive satellite signals, the system experiences a signal outage. In such situations, an Inertial Measurement Unit (IMU) and Light Detection and Ranging (LiDAR)canserve as supporting sensors. Nevertheless, each sensor has its limitations: IMU tends to accumulate drift over time, while LiDAR odometry is prone to cumulative errors during measurements. This study utilizes a fusion method combining LiDAR odometry and IMU data using an Extended Kalman Filter (EKF) to estimate position during GNSS outages. The data from IMU and LiDAR is optimized through a Two-step Joint Optimization approach, aiming to enhance the accuracy of both sensor measurements. The optimized data is then processed using the EKF. This research aims to implement and analyze the performance of IMU-LiDAR sensor fusion using the EKF method and to improve positioning accuracy during GNSS outages. The results of this study show that IMU-LiDAR fusion can serve as a replacement for GNSS during outages, as demonstrated through an EKF simulation that achieved an accuracy with an RMSE of 0.0691 meters for the East position and 0.0405 meters for the North position.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	IMU-LiDAR Integration, GNSS Outage, Extended Kalman Filter (EKF), Second Step Joint Optimization, IMU-LiDAR Integration, GNSS Outage, Extended Kalman Filter (EKF), Second Step Joint Optimization,
Subjects:	Q Science > QA Mathematics > QA402.3 Kalman filtering.
Divisions:	Faculty of Science and Data Analytics (SCIENTICS) > Mathematics > 44201-(S1) Undergraduate Thesis
Depositing User:	Husniyyah Husniyyah
Date Deposited:	28 Jul 2025 04:28
Last Modified:	28 Jul 2025 04:28
URI:	http://repository.its.ac.id/id/eprint/122073

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