Skip to Main Content
Frequently Asked Questions
Submit an ETD
Global Search Box
Need Help?
Keyword Search
Participating Institutions
Advanced Search
School Logo
Files
File List
Bruns_Thesis_Final.pdf (1.18 MB)
ETD Abstract Container
Abstract Header
Lidar-based Vehicle Localization in an Autonomous Valet Parking Scenario
Author Info
Bruns, Christian
Permalink:
http://rave.ohiolink.edu/etdc/view?acc_num=osu1461236677
Abstract Details
Year and Degree
2016, Master of Science, Ohio State University, Electrical and Computer Engineering.
Abstract
Accurate localization is essential to the safe and effective functioning of an autonomous vehicle. In an autonomous valet parking system, the vehicle must be able to estimate its position in the global coordinate frame in order to plan its path and avoid obstacles. Furthermore, precise localization information is necessary for feedback for the control algorithms governing both general parking lot navigation as well as various parking maneuvers. This thesis explores the application of a real-time LIDAR-based landmark sensing scheme combined with a popular simultaneous localization and mapping method known as FastSLAM. The sensing algorithm extracts vertical objects from a 3D Velodyne lidar scan by applying a connected components algorithm to a 2D occupancy grid that is built from the scan. These landmarks are associated robustly from frame to frame in FastSLAM, which is essentially a Rao-Blackwellized particle filter where each particle uses 2D Kalman Filters to estimate the positions of known landmarks. The localization algorithm is tested using data collected from driving and performing parking maneuvers in a typical parking lot. Simulated data is also generated to verify the algorithm and to test its ability to handle varying levels of sensor error and landmark density.
Committee
Umit Ozguner (Advisor)
Andrea Serrani (Committee Member)
Keith Redmill (Committee Member)
Pages
57 p.
Subject Headings
Electrical Engineering
Keywords
SLAM
;
autonomous parking
;
localization
Recommended Citations
Refworks
EndNote
RIS
Mendeley
Citations
Bruns, C. (2016).
Lidar-based Vehicle Localization in an Autonomous Valet Parking Scenario
[Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1461236677
APA Style (7th edition)
Bruns, Christian.
Lidar-based Vehicle Localization in an Autonomous Valet Parking Scenario.
2016. Ohio State University, Master's thesis.
OhioLINK Electronic Theses and Dissertations Center
, http://rave.ohiolink.edu/etdc/view?acc_num=osu1461236677.
MLA Style (8th edition)
Bruns, Christian. "Lidar-based Vehicle Localization in an Autonomous Valet Parking Scenario." Master's thesis, Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1461236677
Chicago Manual of Style (17th edition)
Abstract Footer
Document number:
osu1461236677
Download Count:
2,090
Copyright Info
© 2016, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.