Open AccessKey‐layered normal distributions transform for point cloud registration
Author(s) -
Hong Hyunki,
Lee B.H.
Publication year - 2015
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2015.2323
Subject(s) - key (lock) , matching (statistics) , algorithm , point cloud , computer science , layer (electronics) , cloud computing , point (geometry) , mathematics , artificial intelligence , geometry , materials science , statistics , computer security , composite material , operating system
A new scan matching algorithm is proposed using the concept of key layers. In the conventional multi‐layered normal distributions transform (MLNDT), the number of layers and iterations per layer are fixed and mismatches in point clouds occur due to the limited number of optimising iterations per layer. Moreover, the accuracy of registration is low and the number of layers is heuristically determined in MLNDT. The proposed key‐layered normal distributions transform (KLNDT) works well with both enhanced success rate and accuracy. It is also possible for KLNDT to register in higher layers than the traditional MLNDT.