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

Kotiba Aldibs; Oğuz Mısır

Article Details

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

Authors:

Kotiba Aldibs | Oğuz Mısır

Publisher:

Bitlis Eren University

Main Topics:

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

Journal:

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

Document Type:

Article

Publication Date:

3/2025

Document Language:

English

Document ID (Org No.):

2001343810

pISSN:

2147-3129

Keywords:

Abstract:

 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.

Article Details

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

Authors: Kotiba Aldibs | Oğuz Mısır

Publisher: Bitlis Eren University

Main Topics: Mechatronics Engineering | Simulation | Modelling | and Programming of Mechatronics Systems

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

Document Type: Article

Publication Date: 3/2025

Document Language: English

Document ID (Org No.): 2001343810

pISSN: 2147-3129

Mobile Robot

autonomous

autonomous

Kalman Filter

Object Tracking

Issues

Authors:

Kotiba Aldibs | Oğuz Mısır

Publisher:

Bitlis Eren University

Main Topics:

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

Journal:

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

Document Type:

Article

Publication Date:

3/2025

Document Language:

English

Document ID (Org No.):

2001343810

pISSN:

2147-3129