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Lidar-based Vehicle Localization in an Autonomous Valet Parking Scenario

Bruns, Christian

Abstract Details

2016, Master of Science, Ohio State University, Electrical and Computer Engineering.
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.
Umit Ozguner (Advisor)
Andrea Serrani (Committee Member)
Keith Redmill (Committee Member)
57 p.

Recommended Citations

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)