Paper Info
Title
Improving autonomous underwater grasp specification using primitive shape fitting in point clouds.
Abstract
This paper presents a research in progress towards autonomous underwater robot manipulation. Current research in underwater robotics intends to increase the autonomy of intervention operations that require physical interaction. Autonomous grasping is still a very challenging skill, especially in underwater environments, with highly unstructured scenarios, limited availability of sensors and adverse conditions that affect the robot perception and control systems in various degrees. To tackle those issues, we propose the use of vision and segmentation techniques that aim to improve the specification of grasping operations on underwater primitive shaped objects. Several sources of stereo information are used to gather 3D information in order to obtain a model of the object. Using a RANSAC primitive shape recognition algorithm, the model parameters are estimated and a set of feasible grasps are computed. This approach is validated in simulation and the quality of different 3D reconstructions from both real and virtual scenarios is analyzed.
Year
DOI
Venue
2014
10.3233/978-1-61499-452-7-45
Frontiers in Artificial Intelligence and Applications
Keywords
Field
DocType
underwater autonomous grasping,grasp specification,point cloud,RANSAC,shape fitting,UWSim underwater realistic simulator
Computer vision,GRASP,Artificial intelligence,Point cloud,Geography,Shape fitting,Underwater
Conference
Volume
ISSN
Citations 
269
0922-6389
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
D. Fornas172.31
J. Sales2447.28
Antonio Peñalver391.40
Javier Pérez4212.43
J. Javier Fernandez5323.20
Pedro J. Sanz627837.89