Open AccessShape Matching for Robot Mapping
Author(s) -
Diedrich Wolter,
Longin Jan Latecki
Publication year - 2004
Publication title -
lecture notes in computer science
Language(s) - English
Resource type - Book series
SCImago Journal Rank - 0.249
H-Index - 400
eISSN - 1611-3349
pISSN - 0302-9743
DOI - 10.1007/978-3-540-28633-2_73
Subject(s) - computer science , matching (statistics) , computer vision , artificial intelligence , metric (unit) , representation (politics) , robot , geometric primitive , similarity (geometry) , motion planning , visibility , range (aeronautics) , object (grammar) , path (computing) , measure (data warehouse) , image (mathematics) , mathematics , data mining , statistics , operations management , physics , materials science , optics , composite material , politics , political science , law , economics , programming language
We present a novel geometric model for robot mapping based on shape. Shape similarity measure and matching techniques originating from computer vision are specially redesigned for matching range scans. The fundamental geometric representation is a structural one, polygonal lines are ordered according to the cyclic order of visibility. This approach is an improvement of the underlying geometric models of today's SLAM implementations, where shape matching allows us to disregard pose es- timations. The object-centered approach allows for compact represen- tations that are well-suited to bridge the gap from metric information needed in path planning to more abstract, i.e. topological or qualitative spatial knowledge desired in complex navigational tasks.
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