A tree-structured perception approach for robot operations in modeling of unknown targets

Authors:
S. Y. Chen;Qiu Guan;Gang Xiao;Chunyan Yao;Wanliang Wang
Affiliations:
College of Information Engineering, Zhejiang University of Technology, China and National Laboratory of Pattern Recognition, Chinese Academy of Sciences, China;College of Information Engineering, Zhejiang University of Technology, China and National Laboratory of Pattern Recognition, Chinese Academy of Sciences, China;College of Information Engineering, Zhejiang University of Technology, China and National Laboratory of Pattern Recognition, Chinese Academy of Sciences, China;College of Information Engineering, Zhejiang University of Technology, China and National Laboratory of Pattern Recognition, Chinese Academy of Sciences, China;College of Information Engineering, Zhejiang University of Technology, China and National Laboratory of Pattern Recognition, Chinese Academy of Sciences, China
Venue:
ISCGAV'04 Proceedings of the 4th WSEAS International Conference on Signal Processing, Computational Geometry & Artificial Vision
Year:
2004

Citing 4
Cited 0

A Solution to the Next Best View Problem for Automated Surface Acquisition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Active modeling of 3-D objects: planning on the next best pose (NBP) for acquiring range images

NRC '97 Proceedings of the International Conference on Recent Advances in 3-D Digital Imaging and Modeling
Computations on a spherical view space for efficient planning of viewpoints in 3-D object modeling

3DIM'99 Proceedings of the 2nd international conference on 3-D digital imaging and modeling
Automatic sensor placement for model-based robot vision

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Quantified Score

Hi-index	0.00

Visualization

Abstract

For automatic modeling of three-dimensional unknown objects or environments by an active robot system, the perception strategy is a critical issue since sensing operations have to be decided dynamically and sequentially. This paper presents a tree-structured approach for merging, splitting, and viewpoint deciding. Further development of the algorithms will lead to a very robust method for applying in such complex vision tasks. Repetitive processes, system states, and complete condition are also discussed for exploring the unknown portion of the object or the environment. This paper also treats uncertainties and occlusions, as well as many robot environmental or sensing constraints. Redundant viewpoints are eliminated and new views are planned according to a max-min criterion for achieving lowest cost for robot operations.