Paper Info
Title
Environment Characterization Using Laplace Eigenvalues
Abstract
This paper introduces a new methodology for environment characterization. This methodology is based on analysis of the eigenvalues of Laplace-Beltrami operator over 3 dimensional point clouds. Recognizing revisited places can be facilitated by characterizing the environment through a descriptor. The idea of analyzing point clouds using the eigenvalues of Laplace-Beltrami operator for characterization of an environment can be used for place detection which is a critical functionality of autonomous mobile robots. Place detection is a requirement for transition detection in multi environment missions, common frame identification in multi robot mapping, and detection of previously visited location in SLAM for loop closure phase.
Year
Venue
Keywords
2016
2016 14TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION, ROBOTICS AND VISION (ICARCV)
place detection, transition detection, Lalpace Beltrami spectra, eigenvalue analysis, 3D point cloud analysis, environment characterization
Field
DocType
ISSN
Closure phase,Computer science,Control engineering,Operator (computer programming),Artificial intelligence,Simultaneous localization and mapping,Eigenvalues and eigenvectors,Computer vision,Robot kinematics,Algorithm,Point cloud,Mobile robot,For loop
Conference
2474-2953
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Ehsan Mihankhah101.69
Danwei Wang21529175.13