SHERP

Probabilistic Estimation of Incomplete Map Using Gaussian Process

Cited 0 time in webofscience Cited 0 time in scopus
Authors
레너드 박
Advisor
오성회
Major
공과대학 전기공학부
Issue Date
2013
Publisher
서울대학교 대학원
Keywords
Gaussian ProcessMobile Sensor NetworkEstimation of map. Kernel
Description
학위논문 (석사)-- 서울대학교 대학원 : 전기공학부, 2013. 8. 오성회.
Abstract
Gaussian process is a powerful probabilistic estimation tool which is used widely in engineering fields such as Computer vision, Robotics and sensor networks, etc. This thesis implemented an estimation algorithm of the total map with sparse sensing data using Gaussian Process. In the implemented algorithm, two kinds of kernel functions are applied to the spatial Gaussian Process model
squared exponential kernel and neural network kernel. The performance of the proposed algorithm was verified by the experiments with a simple mobile sensor network. To construct a simple mobile sensor network based on ROS (Robot Operating System) platform, a two wheeled mobile robot (Pioneer3DX) and a two dimensional laser scanner (SICKlms200) are used.
Language
English
URI
http://hdl.handle.net/10371/123226
Files in This Item:
Appears in Collections:
College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Electrical and Computer Engineering (전기·정보공학부)Theses (Master's Degree_전기·정보공학부)
  • mendeley

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

Browse