Autonomous Trail Following

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dc.contributor.advisorJenkin, Michael R.
dc.creatorSefid, Masoud Hoveidar
dc.date.accessioned2018-05-28T12:48:16Z
dc.date.available2018-05-28T12:48:16Z
dc.date.copyright2017-11-06
dc.date.issued2018-05-28
dc.date.updated2018-05-28T12:48:16Z
dc.degree.disciplineComputer Engineering
dc.degree.levelMaster's
dc.degree.nameMASc - Master of Applied Science
dc.description.abstractTrails typically lack standard markers that characterize roadways. Nevertheless, trails are useful for off-road navigation. Here, trail following problem is approached by identifying the deviation of the robot from the heading direction of the trail by fine-tuning a pre-trained Inception-V3 [1] network. Key questions considered in this work include the required number, nature and geometry of the cameras and how trail types -- encoded in pre-existing maps -- can be exploited in addressing this task. Through evaluation of representative image datasets and on-robot testing we found: (i) that although a single camera cannot estimate angular deviation from the heading direction, but it can reliably detect that the robot is, or is not, following the trail; (ii) that two cameras pointing towards the left and the right can be used to estimate heading reliably within a differential framework; (iii) that trail nature is a useful tool for training networks for different trail types.
dc.identifier.urihttp://hdl.handle.net/10315/34504
dc.language.isoen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectRobotics
dc.subject.keywordsAutonomous trail following
dc.subject.keywordsRobotics
dc.subject.keywordsComputer vision
dc.subject.keywordsConvolutional neural networks
dc.subject.keywordsAutonomous driving
dc.titleAutonomous Trail Following
dc.typeElectronic Thesis or Dissertation

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