Rancang Bangun Sistem Persepsi Dan Lokalisasi Pada Autonomous Car Dengan Menggunakan Algoritma Extended Kalman Filter

Nyoto, Alexander Buyung Teva Susilo (2023) Rancang Bangun Sistem Persepsi Dan Lokalisasi Pada Autonomous Car Dengan Menggunakan Algoritma Extended Kalman Filter. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Perkembangan teknologi dalam bidang elektronika telah mendorong kemajuan pesat autonomous vehicle. Salah satu aspek penting dalam kendaraan otonom adalah sistem persepsi, yang berperan dalam mengenali lingkungan sekitar. Tujuan dilaksanakan nya penelitian tugas akhir ini adalah untuk mengetahui bagaimana hasil presepsi dan lokalisasi pada autonomous car dengan menggunakan algoritma extended kalman filter dan mengetaui performansinya. Dilakukan perancangan purwarupa dengan menggunakan single board computer (SBC) di hubungkan dengan sensor MPU6050, RPLIDAR, dan Odometry. Data dari sensor RPLIDAR menghasilkan mapping dengan menggunakan algoritma Hector-SLAM dan data dari sensor MPU6050 dan Odometry digabungkan dengan menggunakan sensor extended kalman filter. Hasil dari pengukuran dievaluasi dengan dibandingkan dengan keadaan aslinya. Error posisi sumbu x 4 persen dan sumbu y 1 persen ketika diberikan algoritma extended kalman filter. Performansi pada pengukuran memiliki nilai error sebesar 4 persen utnuk pengukuran maksimum.
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The rapid advancement of technology in the field of electronics has significantly propelled the progress of autonomous vehicles. One crucial aspect of autonomous cars is their perception system, which plays a vital role in recognizing the surrounding environment. The purpose of this final project research is to understand the results of perception and localization in an autonomous car using the Extended Kalman Filter algorithm and to assess its performance. A prototype is designed using a Single Board Computer (SBC) connected to MPU6050, RPLIDAR, and Odometry sensors. The data from the RPLIDAR sensor generates a mapping using the Hector-SLAM algorithm, while the data from the MPU6050 and Odometry sensors are fused using the Extended Kalman Filter. The measurement results are evaluated by comparing them with the ground truth. The positional error in the x-axis is 4 percent, and in the y-axis, it is 1 percent when the Extended Kalman Filter algorithm is applied. The performance in measurements shows an error value of 4 percent for maximum measurements.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Extended Kalman Filter, MPU6050, RPLIDAR, Odometry
Subjects:	T Technology > TL Motor vehicles. Aeronautics. Astronautics > TL152.8 Vehicles, Remotely piloted. Autonomous vehicles.
Divisions:	Faculty of Industrial Technology and Systems Engineering (INDSYS) > Physics Engineering > 30201-(S1) Undergraduate Thesis
Depositing User:	Alexander Buyung Teva Susilo Nyoto
Date Deposited:	24 Aug 2023 08:31
Last Modified:	24 Aug 2023 08:31
URI:	http://repository.its.ac.id/id/eprint/101557

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