3D Object Classification using Point Clouds and Deep Neural Network for Automotive Applications

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dc.contributor.authorLarsson, Christian
dc.contributor.authorNorén, Erik
dc.contributor.departmentChalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers)sv
dc.contributor.departmentChalmers University of Technology / Department of Computer Science and Engineering (Chalmers)en
dc.date.accessioned2019-07-05T11:52:15Z
dc.date.available2019-07-05T11:52:15Z
dc.date.issued2019
dc.description.abstractObject identification is a central part of autonomous cars and there are many sensors to help with this. One such sensor is the LIDAR which creates point clouds of the cars surrounding. This thesis evaluates a solution for object identification in 3D point clouds with the help of a neural network. A system named DELIS (DEtection in Lidar Systems), which takes a point cloud generated from a LIDAR as input, is designed. The system consists of two subsystems, one non-machine learning algorithm which segments the point cloud into clusters, one for each object, and a neural network that classifies this clusters. The final output is then the classes and the coordinates of the objects in the point cloud. The result of this thesis is a system named DELIS that can identify between pedestrians, cars, and cyclists.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/256715
dc.language.isoeng
dc.setspec.uppsokTechnology
dc.subjectInformations- och kommunikationsteknik
dc.subjectDatavetenskap (datalogi)
dc.subjectInformation & Communication Technology
dc.subjectComputer Science
dc.title3D Object Classification using Point Clouds and Deep Neural Network for Automotive Applications
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
dc.type.uppsokH
local.programmeSystems, control and mechatronics (MPSYS), MSc
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