Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/78477
Title: Registration using Gaussian mixture map for localization
Authors: Yin, Yisheng
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2019
Abstract: Modern methods for vehicle localization rely on receiving reflectivities of the road markings with light detection and ranging (LIDAR). These reflectivities from the road can deteriorate because of inclement weather conditions or poor road texture which will decrease the accuracy of localization. The improvement in LIDAR technology has brought attention to research in the point set registration for point cloud data (PCD) which needs to be efficient and accurate to be implemented for self-driving cars. In this study, a popular registration approach has been implemented which converts the a priori point cloud map into Gaussian mixture models (GMM), which is 2.5D map with height values. This GMM approach is compared to traditional Iterative Closest Point (ICP) approach in terms of point-to-point distance accuracy and computation time. The robustness of the GMM approach is tested and compared with ICP for different Gaussian noise levels.
URI: http://hdl.handle.net/10356/78477
Schools: School of Electrical and Electronic Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

Files in This Item:
File Description SizeFormat 
Report_5.23 JD.pdf
  Restricted Access
2.35 MBAdobe PDFView/Open

Page view(s)

275
Updated on Jun 12, 2024

Download(s)

9
Updated on Jun 12, 2024

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.