Mapping and 3D reconstruction based on lidar

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dc.contributor.authorWang, Liangyu
dc.contributor.authorGanapati Hegde, Varun
dc.contributor.departmentChalmers tekniska högskola / Institutionen för mekanik och maritima vetenskapersv
dc.contributor.examinerBenderius, Ola
dc.contributor.supervisorBenderius, Ola
dc.date.accessioned2021-12-10T12:07:16Z
dc.date.available2021-12-10T12:07:16Z
dc.date.issued2021sv
dc.date.submitted2020
dc.description.abstractA 3D reconstruction and mapping framework aided by nonlinear filter optimization is introduced in this thesis project. A pipeline consisting of point cloud preprossesing, followed by a robust and rapid non-feature-based normal distributions transform (NDT) registration algorithm was developed for accurate mapping. An unscented kalman filter (UKF) solution to fuse inertial measurement unit (IMU) and global navigation satellite system (GNSS) measurements with augmented continuous turn rate velocity (CTRV) magnitude model is considered to obtain continuous six degree of freedom (6-DOF) pose estimation for accurate localization of the agent. The primary motivation for developing a high-quality mapping and localization system is because they play a key role in advancing towards an autonomous vehicle. Due to the lack of synchronized public datasets with IMU, GNSS, and light detection and ranging (lidar) measurements, the initial implementation of the mapping solution was tested over outdoor dataset from cars. The final phase of the project development is aimed at testing and tuning the program for the custom maritime dataset.sv
dc.identifier.coursecodeMMSX30sv
dc.identifier.urihttps://hdl.handle.net/20.500.12380/304397
dc.language.isoengsv
dc.relation.ispartofseries2021:58sv
dc.setspec.uppsokTechnology
dc.subjectmappingsv
dc.subject3D reconstructionsv
dc.subjectlidarsv
dc.subjectKalman filtersv
dc.titleMapping and 3D reconstruction based on lidarsv
dc.type.degreeExamensarbete för masterexamensv
dc.type.uppsokH
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