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Semantic Visual-Inertial Mapping System for Tree Maturity Determination

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dc.contributor.advisor Dailey, Matthew N.
dc.contributor.author Wongsuwan, Kandith
dc.contributor.other Ekpanyapong, Mongkol
dc.contributor.other Luong, Huynh Trung
dc.date.accessioned 2020-02-25T04:23:07Z
dc.date.available 2020-02-25T04:23:07Z
dc.date.issued 2020-05
dc.identifier.other AIT
dc.identifier.uri http://www.cs.ait.ac.th/xmlui/handle/123456789/965
dc.description 71 p. en_US
dc.description.abstract We present a SLAM system, including a post-processing pipeline intended for tree maturity monitoring applications that uses stereo cameras, an IMU, and object information as pipeline inputs. The proposed SLAM methods based on ORB-SLAM2, produces a point cloud and localizes tree landmarks which are passed through the post-processing component to estimate tree trunk perimeters. We align landmarks with the world gravity vector, making it easy to extract tree trunk point clouds from the SLAM map in place of a direct 3D geometrical solution, which we find that would be computationally expensive. In experimental results, the SLAM system has good performance in SLAM pose estimation on the ETH3D dataset while also detecting object landmarks. In addition, the system can approximate tree perimeters, with a higher tree detection rate than a direct point cloud geometrical analysis approach. en_US
dc.description.sponsorship Royal Thai Government Fellowship en_US
dc.publisher AIT en_US
dc.subject Simultaneous Localization and Mapping en_US
dc.subject Object Detection en_US
dc.subject Deep Learning en_US
dc.subject Stereo Vision en_US
dc.subject Unmanned Aerial Vehicle en_US
dc.title Semantic Visual-Inertial Mapping System for Tree Maturity Determination en_US
dc.type Thesis en_US


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