Publication:
Detecting negative obstacle using Kinect sensor

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dc.citedby12
dc.contributor.authorGhani M.F.A.en_US
dc.contributor.authorSahari K.S.M.en_US
dc.contributor.authorid56158540700en_US
dc.contributor.authorid57218170038en_US
dc.date.accessioned2023-05-29T06:38:21Z
dc.date.available2023-05-29T06:38:21Z
dc.date.issued2017
dc.descriptionData 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; Robotsen_US
dc.description.abstractA 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.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1177/1729881417710972
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85021882278
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85021882278&doi=10.1177%2f1729881417710972&partnerID=40&md5=709fae13998718890ec79a5f54eb0538
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23196
dc.identifier.volume14
dc.publisherSAGE Publications Inc.en_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleInternational Journal of Advanced Robotic Systems
dc.titleDetecting negative obstacle using Kinect sensoren_US
dc.typeArticleen_US
dspace.entity.typePublication
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