Utilization of Quadnocular Stereo Vision for Simultaneous Localization and Mapping in Autonomous Vehicles

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dc.contributor.authorAndersson, Robert
dc.contributor.authorNoresson, Oskar
dc.contributor.departmentChalmers tekniska högskola / Institutionen för tillämpad mekaniksv
dc.contributor.departmentChalmers University of Technology / Department of Applied Mechanicsen
dc.date.accessioned2019-07-03T13:43:06Z
dc.date.available2019-07-03T13:43:06Z
dc.date.issued2015
dc.description.abstractThe introduction of autonomous vehicles in commercial applications has an enormous potential to drastically improve both working conditions and productivity. Among the many challenges of realizing autonomous driving, precisely determining the vehicle's pose and constructing an accurate map of an unknown environment might be the two most crucial to overcome. Simultaneously dealing with both these problems is known as SLAM, simultaneous localization and mapping. We have designed and implemented a stereo camera system with which SLAM is performed using only visual information. The proposed system achieves multi-range depth resolution by utilizing four cameras and is implemented exclusively with the help of open-source libraries. Depth images from separate camera pairs are computed in real time with a correlation based block matching algorithm and merged employing a perspective transform. The joint depth map and the corresponding RGB image are then used by a graph-based SLAM algorithm to produce a trajectory estimate and a probabilistic 3D voxel grid map. The system was furthermore evaluated on simulated and real data. The evaluations suggested that with the current configuration it is not motivated to merge depth images from more than two camera pairs, since adding an extra camera pair considerably decreases the frame rate without necessarily improving the quality of the resulting depth map. A number of different image feature extractors were investigated which revealed that binary feature descriptors are suitable for landmark representation in visual SLAM. We can conclude that the system is indeed a fully working prototype, but in order to increase robustness and accuracy to a degree that it is useful in a commercial application a number of future improvements are needed.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/219226
dc.language.isoeng
dc.relation.ispartofseriesDiploma work - Department of Applied Mechanics, Chalmers University of Technology, Göteborg, Sweden : 2015:06
dc.setspec.uppsokTechnology
dc.subjectFarkostteknik
dc.subjectGrundläggande vetenskaper
dc.subjectHållbar utveckling
dc.subjectInnovation och entreprenörskap (nyttiggörande)
dc.subjectTransport
dc.subjectVehicle Engineering
dc.subjectBasic Sciences
dc.subjectSustainable Development
dc.subjectInnovation & Entrepreneurship
dc.subjectTransport
dc.titleUtilization of Quadnocular Stereo Vision for Simultaneous Localization and Mapping in Autonomous Vehicles
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
dc.type.uppsokH
local.programmeSystems, control and mechatronics (MPSYS), MSc
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