Title :
DP-Fusion: A generic framework for online multi sensor recognition
Author :
Liu, Ming ; Wang, Lujia ; Siegwart, Roland
Author_Institution :
Autonomous Syst. Lab., ETH Zurich, Zurich, Switzerland
Abstract :
Multi sensor fusion has been widely used in recognition problems. Most existing works highly depend on the calibration between different sensors, but less on modeling and reasoning of the co-incidence of multiple hints. In this paper, we propose a generic framework for recognition and clustering problem using a non-parametric Dirichlet hierarchical model, named DP-Fusion. It enables online labeling, clustering and recognition of sequential data simultaneously, while considering multiple types of sensor readings. The algorithm is data-driven, which does not depend on priorknowledge of the data structure. The results show the feasibility and reliability against noise data.
Keywords :
nonparametric statistics; pattern clustering; sensor fusion; DP-fusion; clustering problem; data clustering; data structure; multisensor fusion; noise data; nonparametric Dirichlet hierarchical model; online labeling; online multisensor recognition; recognition problem; sensor calibration; sensor reading; sequential data recognition; Approximation algorithms; Approximation methods; Clustering algorithms; Data models; Inference algorithms; Robot sensing systems; Sensor fusion;
Conference_Titel :
Multisensor Fusion and Integration for Intelligent Systems (MFI), 2012 IEEE Conference on
Conference_Location :
Hamburg
Print_ISBN :
978-1-4673-2510-3
Electronic_ISBN :
978-1-4673-2511-0
DOI :
10.1109/MFI.2012.6343031
