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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Nordin Abu Bakar
Universiti Teknologi MARA
Electrical & Electronics Engineering

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Visual analytics of 3D LiDAR point clouds in robotics operating systems Alia Mohd Azri; Shuzlina Abdul-Rahman; Raseeda Hamzah; Zalilah Abd Aziz; Nordin Abu Bakar
Bulletin of Electrical Engineering and Informatics Vol 9, No 2: April 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (329.326 KB) | DOI: 10.11591/eei.v9i2.2061

Abstract

This paper presents visual analytics of 3D LiDAR point clouds in robotics operating system. In this study, experiment on simultaneous localization and mapping (SLAM) using point cloud data derived from the light detection and ranging (LiDAR) technology is conducted. We argue that one of the weaknesses of the SLAM algorithm is in the localization process of the landmarks. Existing algorithms such as grid mapping and monte carlo have limitations in dealing with 3D environment data that have led to less accurate estimation. Therefore, this research proposes the SLAM algorithm based on real-time appearance-based (RTAB) and makes use of the red green blue (RGB) camera for visualisation. The algorithm was tested by using the map data that was collected and simulated on the robot operating system (ROS) in Linux environment. We present the results and demonstrates that the map produced by RTAB is better compared to its counterparts. In addition, the probability for the estimated location is improved which allows for better vehicle maneuverability.
I-OnAR: a rule-based machine learning approach for intelligent assessment in an online learning environment Shaiful Bakhtiar bin Rodzman; Nordin Abu Bakar; Yun-Huoy Choo; Syed Ahmad Aljunid; Normaly Kamal Ismail; Nurazzah Abd Rahman; Marshima Mohd Rosli
Indonesian Journal of Electrical Engineering and Computer Science Vol 17, No 2: February 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v17.i2.pp1021-1028

Abstract

Intelligent systems are created to automate decision making process that is similar to human intelligence. Incorporating intelligent component has achieved promising results in many applications, including in education. Intelligence modules in a tutoring system would bring the application and its capability closer to a human's ability to serve its human users and to solve problems. However, the majority of the online learning provided in the literature review especially in Malaysia, normally only provide the lecture notes, assignments and tests and rarely suggest or give feedbacks on what the students should study or do next in order to fully understand the subjects. Hence, the researchers propose an online learning environment called Intelligent Online Assessment and Revision (I-OnAR). It facilitates the learning process at multiple learning phases such as test creation, materials revision, feedback for improvement and performance analysis. These components are incorporated into the tutoring system to assist self-pace learning at anytime and anywhere. The intelligent agent uses a Rule-based Machine Learning method for the adaptive capabilities such as automated test creation and feedbacks for improvement. The system has been tested on a group of students and found to be useful to support learning process. The results have shown that 60% of the subjects’ performance have improved with the help of the system. The students were given feedbacks on the topic they did poorly as well as how to improve their performance. This proves that the Intelligent Online Assessment and revision (I-OnAR) can be a useful tool to help online students intelligently, systematically and efficiently. For the future works, the researchers would like to apply the usage of other techniques such as Fuzzy Logic to strengthen the analysis and decision of the current system.
Simulation of simultaneous localization and mapping using 3D point cloud data Shuzlina Abdul-Rahman; Mohamad Soffi Abd Razak; Aliya Hasanah Binti Mohd Mushin; Raseeda Hamzah; Nordin Abu Bakar; Zalilah Abd Aziz
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 2: November 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i2.pp941-949

Abstract

Abstract—This paper presents a simulation study of Simultaneous Localization and Mapping (SLAM) using 3D point cloud data from Light Detection and Ranging (LiDAR) technology.  Methods like simulation is useful to simplify the process of learning algorithms particularly when collecting and annotating large volumes of real data is impractical and expensive. In this study, a map of a given environment was constructed in Robotic Operating System platform with Gazebo Simulator. The paper begins by presenting the most currently popular algorithm that are widely used in SLAM namely Extended Kalman Filter, Graph SLAM and Fast SLAM. The study performed the simulations by using standard SLAM with Turtlebot and Husky robots. Husky robot was further compared with ACML algorithm. The results showed that Hector SLAM could reach the goal faster than ACML algorithm in a pre-defined map. Further studies in this field with other SLAM algorithms would certainly beneficial to many parties due to the demands of robotic application.
Co-Authors Alia Mohd Azri Aliya Hasanah Binti Mohd Mushin Marshima Mohd Rosli Mohamad Soffi Abd Razak Normaly Kamal Ismail Nurazzah Abd Rahman Raseeda Hamzah Shaiful Bakhtiar bin Rodzman Shuzlina Abdul-Rahman Syed Ahmad Aljunid Yun-Huoy Choo Zalilah Abd Aziz