Title
Multi-scale point and line range data algorithms for mapping and localization
Abstract
Abstractó This paper presents a multi-scale point and line based representation of two-dimensional range scan data. The techniques are based on a multi-scale Hough transform and a tree representation of the environment’s features. The multi- scale representation can lead to improved robustness and com- putational efciencies in basic operations, such as matching and correspondence, that commonly arise in many localization and mapping procedures. For multi-scale matching and correspon- dence we introduce a,, criterion that is calculated from the estimated variance in position of each detected line segment or point. This improved correspondence method can be used as the basis for simple scan-matching displacement estimation, as a part of a SLAM implementation, or as the basis for solutions to the kidnapped robot problem. Experimental results (using a Sick LMS-200 range scanner) show the effectiveness of our methods.
Year
DOI
Venue
2006
10.1109/ROBOT.2006.1641866
Orlando, FL
Keywords
Field
DocType
Hough transforms,mobile robots,path planning,trees (mathematics),SLAM implementation,displacement estimation,line range data algorithms,multi-scale Hough transform,multi-scale point,tree representation,two-dimensional range scan data
Line segment,Computer science,Algorithm,Hough transform,Robustness (computer science),Feature extraction,Information engineering,Simultaneous localization and mapping,Mobile robot,Kidnapped robot problem
Conference
Volume
Issue
ISSN
2006
1
1050-4729
ISBN
Citations 
PageRank 
0-7803-9505-0
7
0.55
References 
Authors
19
2
Name
Order
Citations
PageRank
Samuel T. Pfister170.55
Burdick, J.W.22988516.87