Abstract
To accurately identify the three-dimensional (3D) structure information of non-cooperative targets in on-orbit service tasks, this paper proposed a 3D reconstruction method combining pose measurement and dense reconstruction. Firstly, based on the depth camera, the Iterative Closest Point (ICP) algorithm is used to realize the pose measurement between each frame of the non-cooperative target. Then, the loopback detection based on the bag-of-words method is adopted to eliminate the cumulative error of pose measurement. At the same time, the sparse 3D model of the non-cooperative target can be obtained by combining feature point extraction and matching. Furthermore, secondary sampling is performed in the tracking and local mapping threads by increasing the dense mapping thread. Then, the loop thread is used to update the pose in turn. After the global pose optimization, a more accurate 3D reconstruction model can be obtained. By using point cloud optimization, the accurate dense 3D reconstruction model of a non-cooperative target is constructed. Finally, numerical simulations and experiments verify the accuracy and effectiveness of the proposed method.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zhang, L., Wu, D.M., Ren, Y.: Pose measurement for non-cooperative target based on visual information. IEEE Access 7, 106179–106194 (2019)
Liu, Y., Zhai, G., Gu, K., et al.: Reduced-reference image quality assessment in free-energy principle and sparse representation. IEEE Trans. Multimed. 20(1), 379–391 (2017)
Peng, J., Xu, W., Liang, B., et al.: Virtual stereovision pose measurement of noncooperative space targets for a dual-arm space robot. IEEE Trans. Instrum. Measur. 69(1), 76–88 (2019)
Liu, Y., Gu, K., Wang, S., et al.: Blind quality assessment of camera images based on low-level and high-level statistical features. IEEE Trans. Multimed. 21(1), 135–146 (2018)
Kang, Z., Yang, J., Yang, Z., et al.: A review of techniques for 3D reconstruction of indoor environments. ISPRS Int. J. Geo-Inf. 9(5), 330 (2020)
Gao, X.-H., Liang, B., Pan, L., Li, Z.-H., Zhang, Y.-C.: A monocular structured light vision method for pose determination of large non-cooperative satellites. Int. J. Control Autom. Syst. 14(6), 1535–1549 (2016). https://doi.org/10.1007/s12555-014-0546-x
Liu, Y., Gu, K., Zhang, Y., et al.: Unsupervised blind image quality evaluation via statistical measurements of structure, naturalness, and perception. IEEE Trans. Circ. Syst. Video Technol. 30(4), 929–9434 (2019)
Gibbs, J.A., Pound, M.P., French, A.P., et al.: Active vision and surface reconstruction for 3D plant shoot modelling. IEEE/ACM Trans. Comput. Biol. Bioinf. 17(6), 1907–1917 (2019)
He, J., Yang, G., Liu, X., et al.: Spatio-temporal saliency-based motion vector refinement for frame rate up-conversion. ACM Trans. Multimed. Comput. Commun. Appl. (TOMM) 16(2), 1–18 (2020)
Kolb, A., Barth, E., Koch, R., et al.: Time-of-flight cameras in computer graphics. In: Computer Graphics Forum, vol. 29, no. 1, pp. 141–159. Blackwell Publishing Ltd, Oxford (2010)
Liu, Y., Gu, K., Li, X., et al.: Blind image quality assessment by natural scene statistics and perceptual characteristics. ACM Trans. Multimed. Comput. Commun. Appl. (TOMM) 16(3), 1–91 (2020)
Jalal, A., Kamal, S., Kim, D.: Shape and motion features approach for activity tracking and recognition from kinect video camera. In: 2015 IEEE 29th International Conference on Advanced Information Networking and Applications Workshops, pp. 445–450. IEEE (2015)
Liu, C., Li, J., Gao, J., et al.: Three-dimensional texture measurement using deep learning and multi-view pavement images. Measurement 172, 108828 (2021)
Liu, Y., Gu, K., Zhai, G., et al.: Quality assessment for real out-of-focus blurred images. J. Vis. Commun. Image Representation 46, 70–80 (2017)
Workman, S., Greenwell, C., Zhai, M., et al.: A method for direct focal length estimation. In: 2015 IEEE International Conference on Image Processing (ICIP), pp. 1369–1373. IEEE (2015)
Hu, H., Gao, J., Zhou, H., et al.: A combined binary defocusing technique with multi-frequency phase error compensation in 3D shape measurement. Optics Lasers Eng. 124, 105806 (2020)
Hu, R., Liu, Y., Gu, K., et al.: Toward a no-reference quality metric for camera-captured images. IEEE Trans. Cybern. (2021)
Zheng, Y., Sugimoto, S., Sato, I., et al.: A general and simple method for camera pose and focal length determination. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 430–437. IEEE (2014)
He, L., Wang, G., Hu, Z.: Learning depth from single images with deep neural network embedding focal length. IEEE Trans. Image Process. 27(9), 4676–4689 (2018)
Liu, Y., Li, X.: No-reference quality assessment for contrast-distorted images. IEEE Access 8, 84105–84115 (2020)
Wallace, L., Lucieer, A., Malenovský, Z., et al.: Assessment of forest structure using two UAV techniques: a comparison of airborne laser scanning and structure from motion (SfM) point clouds. Forests 7(3), 62 (2016)
Gonçalves, G., Gonçalves, D., Gómez-Gutiérrez, Á., et al.: 3D reconstruction of coastal cliffs from fixed-wing and multi-rotor UAS: Impact of SfM-MVS processing parameters, image redundancy and acquisition geometry. Remote Sensing 13(6), 1222 (2021)
Lhuillier, M., Quan, L.: A quasi-dense approach to surface reconstruction from uncalibrated images. IEEE Trans. Pattern Anal. Mach. Intell. 27(3), 418–433 (2005)
Furukawa, Y., Ponce, J.: Accurate, dense, and robust multi-view stereopsis. IEEE Trans. Pattern Anal. Mach. Intell. 23(8), 1362–1376 (2010)
Qader, W.A, Ameen, M.M., Ahmed, B.I.: An overview of bag of words; importance, implementation, applications, and challenges. In: International Engineering Conference (IEC), pp. 200–204. IEEE (2019)
Ahmed, M., Seraj, R., Islam, S.M.S.: The K-means algorithm: a comprehensive survey and performance evaluation. Electronics 9(8), 1295 (2020)
Bai, F., Vidal-Calleja, T., Grisetti, G.: Sparse pose graph optimization in cycle space. IEEE Trans. Rob. 37(5), 1381–1400 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, J., Wang, H., Zhao, Y., Yuan, R., Xu, F. (2023). Dense 3D Reconstruction of Non-cooperative Target Based on Pose Measurement. In: Zhai, G., Zhou, J., Yang, H., Yang, X., An, P., Wang, J. (eds) Digital Multimedia Communications. IFTC 2022. Communications in Computer and Information Science, vol 1766. Springer, Singapore. https://doi.org/10.1007/978-981-99-0856-1_3
Download citation
DOI: https://doi.org/10.1007/978-981-99-0856-1_3
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-0855-4
Online ISBN: 978-981-99-0856-1
eBook Packages: Computer ScienceComputer Science (R0)