A novel octree partitioning approach based on camera parameters for unknown object representation

JeongHyun Seo; JaeWoong Kim; Sukhan Lee; Yeonchool Park

doi:10.1117/12.784520

9 January 2008 A novel octree partitioning approach based on camera parameters for unknown object representation

JeongHyun Seo, JaeWoong Kim, Sukhan Lee, Yeonchool Park

Proceedings Volume 6794, ICMIT 2007: Mechatronics, MEMS, and Smart Materials; 67942S (2008) https://doi.org/10.1117/12.784520
Event: ICMIT 2007: Mechatronics, MEMS, and Smart Materials, 2007, Gifu, Japan

Abstract

This paper proposes an improved octree representation method which is necessary for robot manipulation. To manipulate an object in a certain complex environment, robot needs simple, precise, and task oriented environment representations. Octree is a effective and broadly used three dimensional volumetric representation method to do this. But the problem of the conventional octree representation methods is that it generate many useless and incorrect cells because it generates the octree based on the number of 3D points. To solve these problems, we use the new 'Fill-Factor' concept. This paper introduces the concept and shows the experimental results to show the feasibility of the concept.

Citation Download Citation

JeongHyun Seo, JaeWoong Kim, Sukhan Lee, and Yeonchool Park "A novel octree partitioning approach based on camera parameters for unknown object representation", Proc. SPIE 6794, ICMIT 2007: Mechatronics, MEMS, and Smart Materials, 67942S (9 January 2008); https://doi.org/10.1117/12.784520

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available