Abstract
Robot manipulation tasks like inserting screws and pegs into a hole or automatic screwing require precise tip pose estimation. We propose a novel method to detect and estimate the tip of elongated objects. We demonstrate that our method can estimate tip pose to millimeter-level accuracy. We adopt a probabilistic, appearance-based object detection framework to detect pegs and bits for electric screw drivers. Screws are difficult to detect with feature- or appearance-based methods due to their reflective characteristics. To overcome this we propose a novel adaptation of RANSAC with a parallel-line model. Subsequently, we employ image moments to detect the tip and its pose. We show that the proposed method allows a robot to perform object insertion with only two pairs of orthogonal views, without visual servoing.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)
Chaumette, F.: Image moments: a general and useful set of features for visual servoing. IEEE Trans. Rob. 20(4), 713–723 (2004)
Dantam, N.T., Amor, H.B., Christensen, H.I., Stilman, M.: Online multi-camera registration for bimanual workspace trajectories. In: 14th IEEE-RAS International Conference on Humanoid Robots (Humanoids), pp. 588–593. IEEE (2014)
Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)
Hoffmann, H., Chen, Z., Earl, D., Mitchell, D., Salemi, B., Sinapov, J.: Adaptive robotic tool use under variable grasps. Rob. Auton. Syst. 62(6), 833–846 (2014)
Hu, M.K.: Visual pattern recognition by moment invariants. IRE Trans. Inf. Theory 8(2), 179–187 (1962)
Huang, S., Yamakawa, Y., Senoo, T., Ishikawa, M.: Dynamic compensation by fusing a high-speed actuator and high-speed visual feedback with its application to fast peg-and-hole alignment. Adv. Rob. 28(9), 613–624 (2014)
Liu, S., Xie, W.F., Su, C.Y.: Image-based visual servoing using improved image moments. In: International Conference on Information and Automation, 2009, ICIA 2009, pp. 577–582. IEEE (2009)
Song, H.C., Kim, Y.L., Song, J.B.: Automated guidance of peg-in-hole assembly tasks for complex-shaped parts. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), pp. 4517–4522. IEEE (2014)
Stückler, J., Behnke, S.: Adaptive tool-use strategies for anthropomorphic service robots. In: 14th IEEE-RAS International Conference on Humanoid Robots. IEEE (2014)
Teney, D., Piater, J.: Continuous pose estimation in 2D images at instance and category levels. In: Tenth Conference on Computer and Robot Vision, pp. 121–127. IEEE, May 2013. https://iis.uibk.ac.at/public/papers/Teney-2013-CRV.pdf
Teney, D., Piater, J.: Multiview feature distributions for object detection and continuous pose estimation. Comput. Vis. Image Underst. 125, 265–282 (2014). https://iis.uibk.ac.at/public/papers/Teney-2014-CVIU.pdf
Toussaint, M.: Newton methods for k-order markov constrained motion problems. CoRR abs/1407.0414 (2014). http://arxiv.org/abs/1407.0414
Wang, J., Cho, H.: Micropeg and hole alignment using image moments based visual servoing method. IEEE Trans. Industr. Electron. 55(3), 1286–1294 (2008)
Acknowledgments
The research leading to these results has received funding from the European Communitys Seventh Framework Programme FP7/2007-2013 (Specific Programme Cooperation, Theme 3, Information and Communication Technologies) under grant agreement no. 610878, 3rd HAND.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Shukla, D., Erkent, Ö., Piater, J. (2015). General Object Tip Detection and Pose Estimation for Robot Manipulation. In: Nalpantidis, L., Krüger, V., Eklundh, JO., Gasteratos, A. (eds) Computer Vision Systems. ICVS 2015. Lecture Notes in Computer Science(), vol 9163. Springer, Cham. https://doi.org/10.1007/978-3-319-20904-3_33
Download citation
DOI: https://doi.org/10.1007/978-3-319-20904-3_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-20903-6
Online ISBN: 978-3-319-20904-3
eBook Packages: Computer ScienceComputer Science (R0)