The KIT object models database: An object model database for object recognition,localization and manipulation in service robotics

Abstract

For the execution of object recognition, localization and manipulation tasks, most algorithms use object models. Most models are derived from, or consist of two-dimensional (2D) images and/or three-dimensional (3D) geometric data. The system described in this article was constructed specifically for the generation of such model data. It allows 2D image and 3D geometric data of everyday objects be obtained semi-automatically. The calibration provided allows 2D data to be related to 3D data. Through the use of high-quality sensors, high-accuracy data is achieved. So far over 100 objects have been digitized using this system and the data has been successfully used in several international research projects. All of the models are freely available on the web via a front-end that allows preview and filtering of the data.

Keywords

Domestic robots field and service robotics service robots humanoid robots human-centered and lifelike robotics manipulation planning manipulation recognition sensing and perception computer vision

Get full access to this article

View all access options for this article.

References

Azad

(2010) Integrating Vision Toolkit. http://ivt.sourceforge.net .

Azad

Gockel

Dillmann

(2008) Computer vision: Principles and practice. Brentford: Elektor.

Deng

Dong

Socher

Fei-Fei

(2009) ImageNet: A large-scale hierarchical image database. In: IEEE conference on computer vision and pattern recognition (CVPR‘09), Miami, USA, 20–25 June 2009, pp. 248–255. Piscataway: IEEE Computer Society.

Everingham

Van Gool

Williams

CKI

Winn

Zisserman

(2010) The PASCAL visual object classes (VOC) challenge. International Journal of Computer Vision88(2): 303–338.

Goldfeder

Ciocarlie

Dang

Allen

(2009) The Columbia grasp database. In: IEEE international conference on robotics and automation (ICRA‘09), Kobe, Japan, 12–17 May 2009, pp. 3343–3349. Piscataway: IEEE Computer Society.

Inus Technology (2010a) Rapidform DLL. http://www.rapidform.com/Contents/Product/Skin/ProductETC/category_id/53 .

Inus Technology (2010b) Rapidform Explorer. http://www.rapidform.com/portal/default/Products/index?Category=Products_RapidformEXPLORER_Overview .

Kay

Kajiya

(1986) Ray tracing complex scenes. In: 13th annual conference on computer graphics and interactive techniques (SIGGRAPH ‘86), Dallas, USA, 18–22 August 1986, pp. 269–278. New York: ACM Press.

Lai

Ren

Fox

(2011) A large-scale hierarchical multi-view RGB-D object dataset. In: international conference on robotics and automation (ICRA‘11), Shanghai, China, 9–13 May 2011, pp. 1817–1824. Piscataway: IEEE Computer Society.

10.

Levoy

(2010) Stanford spherical gantry. http://graphics.stanford.edu/projects/gantry .

11.

Samet

(1989) Implementing ray tracing with octrees and neighbor finding. Computers and Graphics 13: 445–460.

12.

Shilane

Min

Kazhdan

Funkhouser

(2004) The Princeton shape benchmark. In: shape modeling international (SMI‘04), Genova, Italy, 7–9 June 2004, pp. 167–178. Washington DC: IEEE Computer Society.

13.

Wagner

Schmalstieg

(2007) ARToolKitPlus for pose tracking on mobile devices. In: 12th computer vision winter workshop (CVWW‘07), St. Lambrecht, Austria, 6–8 February 2007, pp. 139–146.

14.

Wohlers Associates (2010) 3D scanning & reverse engineering overview. http://www.wohlersassociates.com/scanning.html .

15.

Xue

Kasper

Zöllner

Dillmann

(2009) An automatic grasp planning system for service robots. In: 14th international conference on advanced robotics (ICAR‘09), Munich, Germany, 22–26 June 2009, pp. 1–6. Piscataway: IEEE Computer Society.