2020
DOI: 10.11591/eei.v9i2.2061
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Visual analytics of 3D LiDAR point clouds in robotics operating systems

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 … Show more

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Cited by 5 publications
(6 citation statements)
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References 21 publications
(26 reference statements)
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“…However, this research aims to fill the knowledge gap by designing, developing, and evaluating a LIDAR sensor-based wheeled robot system [6,7] which can increase the effectiveness of hostage release operations. With this approach, researchers hope to significantly contribute to solving this important problem and make it safer, more efficient, and more effective in the context of the Indonesian Armed Forces, possibly having broader implications in similar operation scenarios.…”
Section: Introductionmentioning
confidence: 99%
“…However, this research aims to fill the knowledge gap by designing, developing, and evaluating a LIDAR sensor-based wheeled robot system [6,7] which can increase the effectiveness of hostage release operations. With this approach, researchers hope to significantly contribute to solving this important problem and make it safer, more efficient, and more effective in the context of the Indonesian Armed Forces, possibly having broader implications in similar operation scenarios.…”
Section: Introductionmentioning
confidence: 99%
“…It is crucially important to have highly precise decision ability for autonomous robot applications in a dynamic and unknown environment [27]- [29]. The decision process is one of the high-time dependency tasks, especially, detection and identification of obstacles [28], [30], [31].…”
Section: Introductionmentioning
confidence: 99%
“…As the SLAM produces a map of the unknown environment, possible computer vision algorithms for detection and enhancement of image quality can be achieved. One good solution is to use an excellent RGB-Depth camera with a suitable depth range sensor and test the SLAM strategies outdoor and then correct the mismatches and errors occurring underwater environment [6][4] [7]. For the economy and scientific findings, AUVs are exploring the marine environments such as caves, deep sea bed, and shipwrecks.…”
Section: Introductionmentioning
confidence: 99%