Paper Info
Title
Local patch descriptor using deep convolutional generative adversarial network for loop closure detection in SLAM.
Abstract
Recently, the augmented reality and virtual reality fields have been actively researched and a lot of major companies have been aggressively investing the fields. On the core of the research field, the simultaneous localization and mapping (SLAM) algorithm which estimates the camera's position in a global coordinate and simultaneously constructs a 3D environment map firmly settled. Among typical components of modern SLAM framework, we are focusing on a loop closure detection for determining whether the current position of a robot agent was visited previously. The conventional algorithms for the loop closure detection relied on clustering hand-crafted features like SIFT, SURF, and ORB which appear a weakness to handle variations in the image such as a viewpoint change, illumination change, deformation, and occlusion. In this paper, we propose a local patch descriptor using a deep convolutional generative adversarial network to deal with the variations. The experiment result displays the proposed method well clusters the image patches with similar appearances better than the hand-craft features.
Year
Venue
Field
2017
Asia-Pacific Signal and Information Processing Association Annual Summit and Conference
Computer vision,Scale-invariant feature transform,Computer science,Orb (optics),Robot kinematics,Augmented reality,Feature extraction,Artificial intelligence,Simultaneous localization and mapping,Cluster analysis,Reflection mapping
DocType
ISSN
Citations 
Conference
2309-9402
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Dong-Won Shin133.49
Yo-Sung Ho21288146.57