Abstract
3D object pose estimation is an important part of machine vision and robot grasping technology. In order to further improve the accuracy of 3D pose estimation, we propose a novel method based on the point pair feature and weighted voting (WVPPF). Firstly, according to the angle characteristics of the point pair features, the corresponding weight is added to the vote of point pair matching results, and several initial poses are obtained. Then, we exploit initial pose verification to calculate the coincidence between the model and the scene point clouds after the initial pose transformation. Finally, the pose with the highest coincidence is selected as the result. The experiments show that WVPPF can estimate pose effectively for a 60%–70% occlusion rate, and the average accuracy is 17.84% higher than the point pair feature algorithm. At the same time, the WVPPF has good applicability in self-collected environments.
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
Tejani, A., Kouskouridas, R., Doumanoglou, A., Tang, D., Kim, T.K.: Latent–class hough forests for 6 DoF object pose estimation. IEEE Trans. Pattern Anal. Mach. Intell. 40(1), 119–132 (2018)
Li, M.Y., Hashimoto, K.: Accurate object pose estimation using depth only. Sensors 18(4), 1045 (2018)
Liu, S., Cheng, H., Huang, S., Jin, K., Ye, H.: Fast event-inpainting based on lightweight generative adversarial nets. Optoelectron. Lett. 17(8), 507–512 (2021). https://doi.org/10.1007/s11801-021-0201-8
Pan, W., Zhu, F., Hao, Y.M., Zhang, L.M.: Pose measurement method of three-dimensional object based on multi-sensor. Acta Optica Sinica 39(2), 0212007 (2019)
Tejani, A., Tang, D., Kouskouridas, R., Kim, T.-K.: Latent-class hough forests for 3D object detection and pose estimation. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8694, pp. 462–477. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10599-4_30
Chen, T.J., Qin, W., Zou, D.W.: A method for object recognition and pose estimation based on the semantic segmentation. Electron. Technol. 49(1), 36–40 (2020)
Li, S.F., Shi, Z.L., Zhuang, C.G.: Deep learning-based 6D object pose estimation method from point clouds. Comput. Eng. 47(8), 216–223 (2021)
Yu, H.S., Fu, Q., Sun, J., Wu, S.L., Chen, Y.M.: Improved 3D-NDT point cloud registration algorithm for indoor mobile robot. Chin. J. Sci. Instrum. 40(9), 151–161 (2019)
Drost, B., Ulrich, M., Navab, N., Ilic, S.: Model globally, match locally: Efficient and robust 3D object recognition. In: Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 998–1005. IEEE, San Francisco (2010)
Hinterstoisser, S., Lepetit, V., Ilic, S., Holzer, S., Bradski, G., Konolige, K., Navab, N.: Model based training, detection and pose estimation of texture-less 3D objects in heavily cluttered scenes. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012. LNCS, vol. 7724, pp. 548–562. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37331-2_42
Choi, C., Christensen, H.I.: 3D pose estimation of daily objects using an RGB-D camera. In: Proceedings of the 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3342–3349. IEEE, Algarve, Portugal (2012)
Li, M.Y., Koichi, H.: Fast and robust pose estimation algorithm for bin picking using point pair feature. In: Proceedings of the 24th International Conference on Pattern Recognition, pp. 1604–1609. IEEE, Beijing, China (2018)
Funding
Supported by the Major Program of the National Natural Science Foundation of China (Grant No. 61991413).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Lin, S., Li, W., Wang, Y. (2022). Pose Estimation of 3D Objects Based on Point Pair Feature and Weighted Voting. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13456. Springer, Cham. https://doi.org/10.1007/978-3-031-13822-5_34
Download citation
DOI: https://doi.org/10.1007/978-3-031-13822-5_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-13821-8
Online ISBN: 978-3-031-13822-5
eBook Packages: Computer ScienceComputer Science (R0)