Paper Info
Title
Map-building and localization by three-dimensional local features for ubiquitous service robot
Abstract
In this work, we propose a semantic-map building method and localization method for ubiquitous service robot. Our semantic-map is organized by using SIFT feature-based object representation. In addition to semantic map, a vision-based relative localization is employed as a process model of extended Kalman filters, where optical flows and Levenberg-Marquardt least square minimization are incorporated to predict relative robot locations. Thus, robust map-building performances can be obtained even under poor conditions in which localization cannot be achieved by classical odometry-based map-building. To localize robot position and solve kidnap problem, we also propose simple, but fast localization method with a relatively high accuracy by incorporating our semantic-map.
Year
DOI
Venue
2006
10.1007/978-3-540-71789-8_8
ICUCT
Keywords
Field
DocType
robust map-building performance,sift feature-based object representation,classical odometry-based map-building,vision-based relative localization,extended kalman filter,relative robot location,localization method,ubiquitous service robot,robot position,semantic-map building method,three-dimensional local feature,three dimensional,levenberg marquardt,least square,process model,optical flow
Scale-invariant feature transform,Stereo camera,Computer vision,Stereopsis,Odometry,Kalman filter,Artificial intelligence,Monte Carlo localization,Robot,Mathematics,Service robot
Conference
Volume
ISSN
Citations 
4412
0302-9743
0
PageRank 
References 
Authors
0.34
8
4
Name
Order
Citations
PageRank
Youngbin Park145.85
Seungdo Jeong2258.82
Il Hong Suh3780110.60
Byung-Uk Choi45014.62