Autonomous 3D mapping and surveillance of mines with MAVs

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dc.contributor.authorEdwards, Stuart Robert
dc.date.accessioned2018-07-17T12:54:31Z
dc.date.available2018-07-17T12:54:31Z
dc.date.issued2017
dc.descriptionA dissertation Submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, for the degree of Master of Science. 12 July 2017.en_ZA
dc.description.abstractThe mapping of mines, both operational and abandoned, is a long, di cult and occasionally dangerous task especially in the latter case. Recent developments in active and passive consumer grade sensors, as well as quadcopter drones present the opportunity to automate these challenging tasks providing cost and safety bene ts. The goal of this research is to develop an autonomous vision-based mapping system that employs quadrotor drones to explore and map sections of mine tunnels. The system is equipped with inexpensive, structured light, depth cameras in place of traditional laser scanners, making the quadrotor setup more viable to produce in bulk. A modi ed version of Microsoft's Kinect Fusion algorithm is used to construct 3D point clouds in real-time as the agents traverse the scene. Finally, the generated and merged point clouds from the system are compared with those produced by current Lidar scanners.en_ZA
dc.description.librarianLG2018en_ZA
dc.format.extentOnline resource (vii, 61 leaves)
dc.identifier.citationEdwards, Stuart Robert (2017) Autonomous 3D mapping and surveillance of mines with MAVs, University of the Witwatersrand, Johannesburg, https://hdl.handle.net/10539/25005
dc.identifier.urihttps://hdl.handle.net/10539/25005
dc.language.isoenen_ZA
dc.subject.lcshMine safety
dc.subject.lcshMine sanitation
dc.titleAutonomous 3D mapping and surveillance of mines with MAVsen_ZA
dc.typeThesisen_ZA
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