3D Object Pose Estimation Using Chamfer Matching and Flexible CAD File Base

Dewi Mutiara Sari, Vina Wahyuni Eka Putranti


Estimating the object pose is an interesting topic in the industrial robotic vision field. By having an accurate result for detecting object pose, it means the system performs the task as the target in the bin-picking technique. The methods which are developed are varies widely. But the challenge for this paper is estimating a 3D object using mono camera accurately. The object which is used in this paper has the symmetric rotational shape, in this case is the sprayer. In this paper, the camera uses a tool from the Blender Software, such that the ground truth is measurable and it will be the reference for calculating the error. The applied algorithms of this paper are Border Line Extraction Algorithm utilized in the template generation step as the reference template, Directional Chamfer Matching for detecting the coarse pose, and Lavenberg-Marquardt Method to optimize the object pose result. The result achieves the average error of the coarse pose for x and y position (translation pose) are 2.05 mm and 0.71 mm. Meanwhile for the optimized pose, the average error for x and y position (translation pose) are 1.82 mm and 0.24 mm. Regarding the rotational pose, the average error of the rotational coarse pose with respect to x and z axis are 0.01 degree and 0.45 degree. Whereas the average error of the rotational optimized pose with respect to x and z axis are 2.88 degree and 0.82 degree.


3D object pose; borderline extraction; directional chamfer matching; optimized pose; robotic vision; industrial robotic.

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Lysenkov I, Eruhimov V. Pose Refinement of Transparent Rigid Objects with a Stereo Camera. Lecture Notes in Computer Science [Internet]. Springer Berlin Heidelberg; 2013;143–57. Available from: http://dx.doi.org/10.1007/978-3-642-39759-2_11.

Gualtieri M, ten Pas A, Saenko K, Platt R. High precision grasp pose detection in dense clutter. 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) [Internet]. IEEE; 2016 Oct; Available from: http://dx.doi.org/10.1109/iros.2016.7759114.

Ten Pas A, Gualtieri M, Saenko K, Platt R. Grasp Pose Detection in Point Clouds. The International Journal of Robotics Research [Internet]. SAGE Publications; 2017 Oct 30;36(13-14):1455–73. Available from: http://dx.doi.org/10.1177/0278364917735594.

Hossain D, Capi G, Jindai M, Kaneko S. Pick-place of dynamic objects by robot manipulator based on deep learning and easy user interface teaching systems. Industrial Robot: An International Journal [Internet]. Emerald; 2017 Jan 16;44(1):11–20. Available from: http://dx.doi.org/10.1108/ir-05-2016-0140.

Czajewski W, Kołomyjec K. 3D Object Detection and Recognition for Robotic Grasping Based on RGB-D Images and Global Features. Foundations of Computing and Decision Sciences [Internet]. Walter de Gruyter GmbH; 2017 Sep 1;42(3):219–37. Available from: http://dx.doi.org/10.1515/fcds-2017-0011.

Tejani A, Tang D, Kouskouridas R, Kim T-K. Latent-Class Hough Forests for 3D Object Detection and Pose Estimation. Lecture Notes in Computer Science [Internet]. Springer International Publishing; 2014;462–77. Available from: http://dx.doi.org/10.1007/978-3-319-10599-4_30.

Di Castro M, Vera JC, Masi A, Pérez MF. Novel Pose Estimation System for Precise Robotic Manipulation in Unstructured Environment. Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics [Internet]. SCITEPRESS - Science and Technology Publications; 2017; Available from: http://dx.doi.org/10.5220/0006426700500055.

Abbeloos W, Ataer-Cansizoglu E, Caccamo S, Taguchi Y, Domae Y. 3D Object Discovery and Modeling Using Single RGB-D Images Containing Multiple Object Instances. 2017 International Conference on 3D Vision (3DV) [Internet]. IEEE; 2017 Oct; Available from: http://dx.doi.org/10.1109/3dv.2017.00056.

Izatt G, Dai H, Tedrake R. Globally Optimal Object Pose Estimation in Point Clouds with Mixed-Integer Programming. Robotics Research [Internet]. Springer International Publishing; 2019 Nov 28;695–710. Available from: http://dx.doi.org/10.1007/978-3-030-28619-4_49.

Sakcak B, Bascetta L, Ferretti G. Model based Detection and 3D Localization of Planar Objects for Industrial Setups. Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics [Internet]. SCITEPRESS - Science and and Technology Publications; 2016; Available from: http://dx.doi.org/10.5220/0005982503600367.

Liu M-Y, Tuzel O, Veeraraghavan A, Taguchi Y, Marks TK, Chellappa R. Fast object localization and pose estimation in heavy clutter for robotic bin picking. The International Journal of Robotics Research [Internet]. SAGE Publications; 2012 May 8;31(8):951–73. Available from: http://dx.doi.org/10.1177/0278364911436018.

Zeng Z, Yan H. Hidden Line Removal for 2D Cartoon Images. In: Proceedings of the Pan-Sydney Area Workshop on Visual Information Processing - Volume 11. AUS: Australian Computer Society, Inc.; 2001. p. 89–92. (VIP ’01).

Nisha N. Visible Surface Detection Algorithms: A Review. International Journal of Advanced Engineering Research and Science [Internet]. AI Publications; 2017;4(2):147–9. Available from: http://dx.doi.org/10.22161/ijaers.4.2.29.

Li M, Guo B, Zhang W. An Occlusion Detection Algorithm for 3D Texture Reconstruction of multi-View Images. International Journal of Machine Learning and Computing [Internet]. EJournal Publishing; 2017 Oct;7(5):152–5. Available from: http://dx.doi.org/10.18178/ijmlc.2017.7.5.638.

Maarif ES, Pitowarno E, Widodo RT. A Trajectory Generation Method Based on Edge Detection for Auto-Sealant Cartesian Robot. Journal of Mechatronics, Electrical Power, and Vehicular Technology [Internet]. Indonesian Institute of Sciences; 2014 Jul 23;5(1):27. Available from: http://dx.doi.org/10.14203/j.mev.2014.v5.27-36.

DOI: http://dx.doi.org/10.18517/ijaseit.10.2.8336


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