Object Tracking Using Lidar Data Filtered by Minimized Kalman Filter on Turtlebot3 Mobile Robot

Kotiba Aldibs; Oğuz Mısır

Makale detayları

Object Tracking Using Lidar Data Filtered by Minimized Kalman Filter on Turtlebot3 Mobile Robot

Yazarlar:

Kotiba Aldibs | Oğuz Mısır

Yayıncı:

Bitlis Eren University

Ana Başlıklar:

Mechatronics Engineering | Simulation | Modelling | and Programming of Mechatronics Systems

Dergi:

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.14 Issue 1, pp.179-197

Belge Tipi:

Makale

Yayın Tarihi:

3/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001343810

pISSN:

2147-3129

Anahtar kelimeler:

Özet:

 The development of autonomous vehicles requires high accuracy and precision in sensor data for effective interaction with the environment and execution of functions. Processing this data with efficient algorithms positively influences vehicle decision-making. In this study, the TurtleBot3 platform, an ideal simulation model for autonomous vehicles, is used to detect and track nearby objects in the sub-system Robotic Operating System ROS Noetic environment. The lidar sensor data from this platform is refined using interpolation and a minimized Kalman filter to remove noise and irregularities. This approach provides clearer and more reliable measurement data, resulting in more stable and fine-tuned responses in the vehicles motion planning. Compared to the general Kalman filter theory, this method offers faster implementation without relying on the exact error tolerance of the sensor to provide acceptable results.

 The development of autonomous vehicles requires high accuracy and precision in sensor data for effective interaction with the environment and execution of functions. Processing this data with efficient algorithms positively influences vehicle decision-making. In this study, the TurtleBot3 platform, an ideal simulation model for autonomous vehicles, is used to detect and track nearby objects in the sub-system Robotic Operating System ROS Noetic environment. The lidar sensor data from this platform is refined using interpolation and a minimized Kalman filter to remove noise and irregularities. This approach provides clearer and more reliable measurement data, resulting in more stable and fine-tuned responses in the vehicles motion planning. Compared to the general Kalman filter theory, this method offers faster implementation without relying on the exact error tolerance of the sensor to provide acceptable results.

 The development of autonomous vehicles requires high accuracy and precision in sensor data for effective interaction with the environment and execution of functions. Processing this data with efficient algorithms positively influences vehicle decision-making. In this study, the TurtleBot3 platform, an ideal simulation model for autonomous vehicles, is used to detect and track nearby objects in the sub-system Robotic Operating System ROS Noetic environment. The lidar sensor data from this platform is refined using interpolation and a minimized Kalman filter to remove noise and irregularities. This approach provides clearer and more reliable measurement data, resulting in more stable and fine-tuned responses in the vehicles motion planning. Compared to the general Kalman filter theory, this method offers faster implementation without relying on the exact error tolerance of the sensor to provide acceptable results.

 The development of autonomous vehicles requires high accuracy and precision in sensor data for effective interaction with the environment and execution of functions. Processing this data with efficient algorithms positively influences vehicle decision-making. In this study, the TurtleBot3 platform, an ideal simulation model for autonomous vehicles, is used to detect and track nearby objects in the sub-system Robotic Operating System ROS Noetic environment. The lidar sensor data from this platform is refined using interpolation and a minimized Kalman filter to remove noise and irregularities. This approach provides clearer and more reliable measurement data, resulting in more stable and fine-tuned responses in the vehicles motion planning. Compared to the general Kalman filter theory, this method offers faster implementation without relying on the exact error tolerance of the sensor to provide acceptable results.

Makale detayları

Object Tracking Using Lidar Data Filtered by Minimized Kalman Filter on Turtlebot3 Mobile Robot

Yazarlar: Kotiba Aldibs | Oğuz Mısır

Yayıncı: Bitlis Eren University

Ana Başlıklar: Mechatronics Engineering | Simulation | Modelling | and Programming of Mechatronics Systems

Dergi: Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.14 Issue 1, pp.179-197

Belge Tipi: Makale

Yayın Tarihi: 3/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001343810

pISSN: 2147-3129

Mobile Robot

autonomous

autonomous

Kalman Filter

Object Tracking

Sayı

Yazarlar:

Kotiba Aldibs | Oğuz Mısır

Yayıncı:

Bitlis Eren University

Ana Başlıklar:

Mechatronics Engineering | Simulation | Modelling | and Programming of Mechatronics Systems

Dergi:

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.14 Issue 1, pp.179-197

Belge Tipi:

Makale

Yayın Tarihi:

3/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001343810

pISSN:

2147-3129