Improvement of the 2D SLAM system using Kinect sensor for indoor mapping
Simultaneous localization and mapping (SLAM) is an important system for making fully autonomous robot navigation system. A robot must have a good understanding of the environment for navigation. Mobile robot using fixed laser range finder can only detect obstacle on a particular plane level. This ma...
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2023
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my.uniten.dspace-219262023-05-16T10:46:06Z Improvement of the 2D SLAM system using Kinect sensor for indoor mapping Ghani M.F.A. Sahari K.S.M. Kiong L.C. 56158540700 57218170038 57193642839 Simultaneous localization and mapping (SLAM) is an important system for making fully autonomous robot navigation system. A robot must have a good understanding of the environment for navigation. Mobile robot using fixed laser range finder can only detect obstacle on a particular plane level. This may lead to missing of important obstacles that are both above or below the laser scan level. This will cause the map generated to be inaccurate and collision may happen during autonomous navigation. The Microsoft Kinect sensor can replace a laser range finder in a mobile robot as a low cost alternative while providing 3D data. Using depth image data from Kinect, the obstacles between ground surface and 500mm high are projected to horizontal surface, and converted to laser scan data. This will include the entire obstacles that the robot might collide into. Simulations and experiments have been conducted to test out the method in SLAM process in an indoor environment. Both simulation and experimental results show that the proposed method is capable of getting more detailed map compared to the normal 2D map. © 2014 IEEE. Final 2023-05-16T02:46:06Z 2023-05-16T02:46:06Z 2014 Conference Paper 10.1109/SCIS-ISIS.2014.7044753 2-s2.0-84988269553 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988269553&doi=10.1109%2fSCIS-ISIS.2014.7044753&partnerID=40&md5=e6acc7e882dda59a3413aaef9382e69b https://irepository.uniten.edu.my/handle/123456789/21926 7044753 776 781 Institute of Electrical and Electronics Engineers Inc. Scopus |
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Simultaneous localization and mapping (SLAM) is an important system for making fully autonomous robot navigation system. A robot must have a good understanding of the environment for navigation. Mobile robot using fixed laser range finder can only detect obstacle on a particular plane level. This may lead to missing of important obstacles that are both above or below the laser scan level. This will cause the map generated to be inaccurate and collision may happen during autonomous navigation. The Microsoft Kinect sensor can replace a laser range finder in a mobile robot as a low cost alternative while providing 3D data. Using depth image data from Kinect, the obstacles between ground surface and 500mm high are projected to horizontal surface, and converted to laser scan data. This will include the entire obstacles that the robot might collide into. Simulations and experiments have been conducted to test out the method in SLAM process in an indoor environment. Both simulation and experimental results show that the proposed method is capable of getting more detailed map compared to the normal 2D map. © 2014 IEEE. |
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56158540700 |
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56158540700 Ghani M.F.A. Sahari K.S.M. Kiong L.C. |
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Conference Paper |
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Ghani M.F.A. Sahari K.S.M. Kiong L.C. |
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Ghani M.F.A. Sahari K.S.M. Kiong L.C. Improvement of the 2D SLAM system using Kinect sensor for indoor mapping |
author_sort |
Ghani M.F.A. |
title |
Improvement of the 2D SLAM system using Kinect sensor for indoor mapping |
title_short |
Improvement of the 2D SLAM system using Kinect sensor for indoor mapping |
title_full |
Improvement of the 2D SLAM system using Kinect sensor for indoor mapping |
title_fullStr |
Improvement of the 2D SLAM system using Kinect sensor for indoor mapping |
title_full_unstemmed |
Improvement of the 2D SLAM system using Kinect sensor for indoor mapping |
title_sort |
improvement of the 2d slam system using kinect sensor for indoor mapping |
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Institute of Electrical and Electronics Engineers Inc. |
publishDate |
2023 |
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