Lab meeitng 27 July, 2006 (Nelson): Multi-scale Point and Line Range Data Algorithms for Mapping and Localization

Multi-scale Point and Line Range Data Algorithms
for Mapping and Localization

Samuel T. Pfister and Joel W. Burdick

Division of Engineering and Applied Science
California Institute of Technology
Pasadena, California 91125, USA
Email:{sam,jwb}@robotics.caltech.edu

Abstract:
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 multiscale
representation can lead to improved robustness and computational
efficiencies in basic operations, such as matching and
correspondence, that commonly arise in many localization and
mapping procedures. For multi-scale matching and correspondence
we introduce a χ2 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.