An analysis of state covariance of mobile robot navigation in unstructured environment based on ROS
This paper deals with mobile robot navigation in unstructured environment by using Robot Operating System (ROS). ROS is a framework to develop robotic application and it consists of algorithms to build maps, navigate, and interpret sensor data. The system is used to define a condition of mobile robo...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
Springer
2021
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/34467/ http://umpir.ump.edu.my/id/eprint/34467/1/An%20analysis%20of%20state%20covariance%20of%20mobile%20robot.pdf |
| _version_ | 1848824514286190592 |
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| author | Hamzah, Ahmad Lim Zhi, Xian Nur Aqilah, Othman Mohd Syakirin, Ramli Mohd Mawardi, Saari |
| author_facet | Hamzah, Ahmad Lim Zhi, Xian Nur Aqilah, Othman Mohd Syakirin, Ramli Mohd Mawardi, Saari |
| author_sort | Hamzah, Ahmad |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | This paper deals with mobile robot navigation in unstructured environment by using Robot Operating System (ROS). ROS is a framework to develop robotic application and it consists of algorithms to build maps, navigate, and interpret sensor data. The system is used to define a condition of mobile robot navigation in a specific environment to evaluate the estimation performance. The research aims to analyze and investigate the mobile robot movement in unknown environment by using Kalman Filter approach considering uncertainties. Only one LiDAR sensor and one IMU sensor are applied to measure the relative distance and then provide the information for estimation purposes. An experiment of a Turtlebot that can keep track autonomously with collision avoidance has been organized to recognize the mobile robot motions through the application of Kalman Filter. Once the simulation is successfully performed as expected, then only the experimental analysis are organized. The results shown that Kalman Filter can sufficiently estimate the condition of the environment with only depending on a LiDAR and IMU sensors with good performance. Besides, the calculated state covariance is also agreed with the theoretical analysis. |
| first_indexed | 2025-11-15T03:14:14Z |
| format | Conference or Workshop Item |
| id | ump-34467 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:14:14Z |
| publishDate | 2021 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-344672022-11-11T07:04:50Z http://umpir.ump.edu.my/id/eprint/34467/ An analysis of state covariance of mobile robot navigation in unstructured environment based on ROS Hamzah, Ahmad Lim Zhi, Xian Nur Aqilah, Othman Mohd Syakirin, Ramli Mohd Mawardi, Saari T Technology (General) TK Electrical engineering. Electronics Nuclear engineering This paper deals with mobile robot navigation in unstructured environment by using Robot Operating System (ROS). ROS is a framework to develop robotic application and it consists of algorithms to build maps, navigate, and interpret sensor data. The system is used to define a condition of mobile robot navigation in a specific environment to evaluate the estimation performance. The research aims to analyze and investigate the mobile robot movement in unknown environment by using Kalman Filter approach considering uncertainties. Only one LiDAR sensor and one IMU sensor are applied to measure the relative distance and then provide the information for estimation purposes. An experiment of a Turtlebot that can keep track autonomously with collision avoidance has been organized to recognize the mobile robot motions through the application of Kalman Filter. Once the simulation is successfully performed as expected, then only the experimental analysis are organized. The results shown that Kalman Filter can sufficiently estimate the condition of the environment with only depending on a LiDAR and IMU sensors with good performance. Besides, the calculated state covariance is also agreed with the theoretical analysis. Springer 2021 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/34467/1/An%20analysis%20of%20state%20covariance%20of%20mobile%20robot.pdf Hamzah, Ahmad and Lim Zhi, Xian and Nur Aqilah, Othman and Mohd Syakirin, Ramli and Mohd Mawardi, Saari (2021) An analysis of state covariance of mobile robot navigation in unstructured environment based on ROS. In: Proceedings of the 11th National Technical Seminar on Unmanned System Technology 2019 (NUSYS’19) , 2-3 December 2019 , Universiti Malaysia Pahang, Kuantan, Pahang, Malaysia. pp. 259-270., 666. ISBN 1876-1100 (Published) https://doi.org/10.1007/978-981-15-5281-6_18 |
| spellingShingle | T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Hamzah, Ahmad Lim Zhi, Xian Nur Aqilah, Othman Mohd Syakirin, Ramli Mohd Mawardi, Saari An analysis of state covariance of mobile robot navigation in unstructured environment based on ROS |
| title | An analysis of state covariance of mobile robot navigation in unstructured environment based on ROS |
| title_full | An analysis of state covariance of mobile robot navigation in unstructured environment based on ROS |
| title_fullStr | An analysis of state covariance of mobile robot navigation in unstructured environment based on ROS |
| title_full_unstemmed | An analysis of state covariance of mobile robot navigation in unstructured environment based on ROS |
| title_short | An analysis of state covariance of mobile robot navigation in unstructured environment based on ROS |
| title_sort | analysis of state covariance of mobile robot navigation in unstructured environment based on ros |
| topic | T Technology (General) TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/34467/ http://umpir.ump.edu.my/id/eprint/34467/ http://umpir.ump.edu.my/id/eprint/34467/1/An%20analysis%20of%20state%20covariance%20of%20mobile%20robot.pdf |