Detecting negative obstacle using Kinect sensor
Data handling; Floors; Mapping; Mobile robots; Navigation; Navigation systems; Obstacle detectors; Above ground level; Autonomous navigation; Laser range scanners; Microsoft kinect; Mobile Robot Navigation; Mobile robotic; Obstacle detection; Obstacles detection; Robots
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2023
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my.uniten.dspace-231962023-05-29T14:38:21Z Detecting negative obstacle using Kinect sensor Ghani M.F.A. Sahari K.S.M. 56158540700 57218170038 Data handling; Floors; Mapping; Mobile robots; Navigation; Navigation systems; Obstacle detectors; Above ground level; Autonomous navigation; Laser range scanners; Microsoft kinect; Mobile Robot Navigation; Mobile robotic; Obstacle detection; Obstacles detection; Robots A robot must have a good understanding of the environment for autonomous navigation. Mobile robot using fixed laser range scanner can only detect obstacle on a plane level. This may cause important obstacles not to be appropriately detected. This will cause the map generated to be inaccurate and collision may actually occur during autonomous navigation. Microsoft Kinect is known to provide a low-cost 3-D data which can be used for mobile robot navigation. Many researchers focused on obstacles above ground level, and not negative obstacles such as holes or stairs. This article proposes the usage of Kinect sensor to detect negative obstacles and converts it into laser scan data. Positive obstacle is defined as the obstacle above the floor surface and negative obstacle is defined as the obstacle below the floor surface. Projection method is used to convert positive obstacle data from Kinect sensor to laser scan data. For negative obstacles detection, farthest point method and virtual floor projection method are used. The laser scan data from positive and negative obstacles are then combined to get an improved laser scan data, which includes all obstacles that are important for a robot to see. The negative obstacle detection methods are tested in simulated indoor environment and also experimental in a real environment. The simulation and experimental results have demonstrated the effectiveness of our proposed method to detect and map negative obstacles. � 2017, � The Author(s) 2017. Final 2023-05-29T06:38:21Z 2023-05-29T06:38:21Z 2017 Article 10.1177/1729881417710972 2-s2.0-85021882278 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021882278&doi=10.1177%2f1729881417710972&partnerID=40&md5=709fae13998718890ec79a5f54eb0538 https://irepository.uniten.edu.my/handle/123456789/23196 14 3 All Open Access, Gold SAGE Publications Inc. Scopus |
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| description |
Data handling; Floors; Mapping; Mobile robots; Navigation; Navigation systems; Obstacle detectors; Above ground level; Autonomous navigation; Laser range scanners; Microsoft kinect; Mobile Robot Navigation; Mobile robotic; Obstacle detection; Obstacles detection; Robots |
| author2 |
56158540700 |
| author_facet |
56158540700 Ghani M.F.A. Sahari K.S.M. |
| format |
Article |
| author |
Ghani M.F.A. Sahari K.S.M. |
| spellingShingle |
Ghani M.F.A. Sahari K.S.M. Detecting negative obstacle using Kinect sensor |
| author_sort |
Ghani M.F.A. |
| title |
Detecting negative obstacle using Kinect sensor |
| title_short |
Detecting negative obstacle using Kinect sensor |
| title_full |
Detecting negative obstacle using Kinect sensor |
| title_fullStr |
Detecting negative obstacle using Kinect sensor |
| title_full_unstemmed |
Detecting negative obstacle using Kinect sensor |
| title_sort |
detecting negative obstacle using kinect sensor |
| publisher |
SAGE Publications Inc. |
| publishDate |
2023 |
| _version_ |
1806428358448250880 |
| score |
13.222552 |