Pose estimation of an uncooperative spacecraft from actual space imagery
Abstract
This paper addresses the preliminary design of a spaceborne monocular vision-based navigation system for on-orbit-servicing and formation-flying applications. The aim is to estimate the pose of a passive space resident object using its known three-dimensional model and single low-resolution two-dimensional images collected on-board the active spacecraft. In contrast to previous work, no supportive means are available on the target satellite (e.g., light emitting diodes) and no a-priori knowledge of the relative position and attitude is available (i.e., lost-in-space scenario). Three fundamental mechanisms – perceptual organisation, true perspective projection, and random sample consensus – are exploited to overcome the limitations of monocular passive optical navigation in space. The preliminary design is conducted and validated making use of actual images collected in the frame of the PRISMA mission at about 700 km altitude and 10 m inter-spacecraft separation.
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The HIL images of SPEED+ involve a half-scale mockup model of the Tango spacecraft from the PRISMA mission.
“The HIL images of SPEED+ involve a half-scale mockup model of the Tango spacecraft from the PRISMA mission [17,18].”
From SPEED+: Next-Generation Dataset for Spacecraft Pose Estimation across Domain Gap (2022) · cites this paper as [17]
D'Amico et al.'s method did not prove robust with respect to spacecraft symmetry.
“Furthermore, it did not prove to be robust with respect to the spacecraft symmetry.”
From Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft (2019) · cites this paper as [15]
D'Amico et al.'s method lacked robustness to illumination and background conditions.
“but it lacked of robustness to illumination and background conditions.”
From Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft (2019) · cites this paper as [15]
D'Amico et al.'s method was tested with the PRISMA image dataset and proved flexible with respect to spacecraft shape.
“Its method was tested with the PRISMA image dataset and proved to be flexible with respect to the spacecraft shape”
From Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft (2019) · cites this paper as [15]
D'Amico et al. [15] used the same feature detection and extraction methods as [13] in combination with a Low-Pass Filter.
“D'Amico et al. [15] used the same feature detection and extraction methods in [13] in combination with a Low-Pass Filter (LPF).”
From Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft (2019) · cites this paper as [15]
The limited amount of realistic space images available in [15], [24] and [9] could not represent all challenging orbital scenarios for which a specific camera-target-Sun-Earth geometry would affect pose estimation accuracy.
“Also, the limited amount of realistic space images available in [15], [24] and [9] could not represent all the 450 challenging orbital scenarios for which a specific camera-target-Sun-Earth geometry would affect the pose estimation accuracy.”
From Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft (2019) · cites this paper as [15]
The architecture in [9] shows improvements over previous IP and pose estimation techniques [15,11,10].
“In particular, this architecture shows improvements with respect to previous IP and pose estimation techniques [15,11,10].”
From Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft (2019) · cites this paper as [15]
SPNv2 is also evaluated on prisma25, which contains 25 labeled spaceborne images of Tango acquired during the rendezvous phase of the PRISMA mission.
“SPNv2 is also evaluated on prisma25 which contains 25 labeled spaceborne images of Tango acquired during the rendezvous phase of the PRISMA mission (D'Amico et al., 2013, 2014).”
From Robust multi-task learning and online refinement for spacecraft pose estimation across domain gap (2024) · cites this paper as D'Amico et al., 2014
The SPEED+ dataset comprises images of the Tango spacecraft from the PRISMA mission, consisting of three distinct domains: synthetic, lightbox, and sunlamp.
“The SPEED+ [22] dataset comprises images of the Tango spacecraft from the PRISMA [49] mission, consisting of three distinct domains, i.e., synthetic, lightbox, and sunlamp.”
From Bridging the Domain Gap in Satellite Pose Estimation: A Self-Training Approach Based on Geometrical Constraints (2024) · cites this paper as [49]
Cites
A Computational Approach to Edge Detection
Canny, J. (1986) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1986.4767851 · Cited by 30,004 · see citing works
Fischler, M. A., & Bolles, R. C. (1981) · Communications of the ACM · doi:10.1145/358669.358692 · Cited by 25,893 · see citing works
Use of the hough transformation to detect lines and curves in pictures
Duda, R. O., & Hart, P. E. (1972) · Communications of the ACM · doi:10.1145/361237.361242 · Cited by 6,633 · see citing works
MLESAC: A New Robust Estimator with Application to Estimating Image Geometry
Torr, P. H. S., & Zisserman, A. (2000) · Computer Vision and Image Understanding · doi:10.1006/cviu.1999.0832 · Cited by 2,402 · see citing works
Three-dimensional object recognition from single two-dimensional images
Lowe, D. G. (1987) · Artificial Intelligence · doi:10.1016/0004-3702(87)90070-1 · Cited by 1,283 · see citing works
Perceptual organization and visual recognition
(1985) · Computer Vision Graphics and Image Processing · doi:10.1016/0734-189x(85)90041-6 · Cited by 1,033 · see citing works
Model-based object pose in 25 lines of code
DeMenthon, D., & Davis, L. S. (1995) · International Journal of Computer Vision · doi:10.1007/bf01450852 · Cited by 1,013 · see citing works
Spaceborne Autonomous Formation-Flying Experiment on the PRISMA Mission
D'Amico, S., Ardaens, J.-S., & Larsson, R. (2012) · Journal of Guidance, Control, and Dynamics · doi:10.2514/1.55638 · Cited by 173 · see citing works
Self-citationFlight Dynamics Challenges of the German On-Orbit Servicing Mission DEOS
Rupp, T., Boge, T., Kiehling, R., & Sellmaier, F. (2009) · elib (German Aerospace Center) · Cited by 67 · see citing works
Prisma Formation Flying Demonstrator: Overview and Conclusions from the Nominal Mission
Bodin, P., Noteborn, R., Larsson, R., Karlsson, T., D’Amico, S., Ardaens, J.-S., Delpech, M., & Bergès, J. (2012) · elib (German Aerospace Center) · Cited by 58 · see citing works
Cropp, A. (2001) · Surrey Research Insight Open Access (The University of Surrey) · Cited by 29 · see citing works
Final commissioning of the PRISMA GPS navigation system
Ardaens, J.-S., D’Amico, S., & Montenbruck, O. (2012) · Journal of Aerospace Engineering Sciences and Applications · doi:10.7446/jaesa.0403.10 · Cited by 28 · see citing works
Formation flying and mission design for Proba-3
Landgraf, M., & Mestreau-Garreau, A. (2012) · Acta Astronautica · doi:10.1016/j.actaastro.2012.03.028 · Cited by 16 · see citing works
Autonomous spacecraft docking using a computer vision system
Ho, C.-C. J., & McClamroch, N. H. (2005) · Proceedings of the IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes · doi:10.1109/cdc.1992.371650 · Cited by 14 · see citing works
An Analysis of Lowe's Model-based Vision System
McIvor, A. M. (1988) · Alvey Vision Conference (AVC) · doi:10.5244/c.2.12 · Cited by 7 · see citing works
Using an autonomous star tracker as formation flying sensor
Jørgensen, J. L., Denver, T., & Jørgensen, P. S. (2004) · Cited by 7 · see citing works
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Opromolla, R., Fasano, G., Rufino, G., & Grassi, M. (2017) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2017.07.001 · Cited by 330 · see citing works
Spacecraft pose estimation dataset (speed)
Sharma, S., Park, T. H., & D'Amico, S. (2026) · Stanford Digital Repository · doi:10.25740/dz692fn7184 · Cited by 304
Self-citationSatellite Pose Estimation Challenge: Dataset, Competition Design, and Results
Kisantal, M., Sharma, S., Park, T. H., Izzo, D., Martens, M., & D'Amico, S. (2020) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2020.2989063 · Cited by 304 · see citing works
“[16] were one of the first to publish pose estimation results using the Hough transform and the Canny edge detector on spaceborne images captured during the rendezvous phase of the PRISMA mission [16], [26].”
Neural Network-Based Pose Estimation for Noncooperative Spacecraft Rendezvous
Sharma, S., & D'Amico, S. (2020) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2020.2999148 · Cited by 196 · see citing works
Satellite Pose Estimation with Deep Landmark Regression and Nonlinear Pose Refinement
Chen, B., Cao, J., Parra, A., & Chin, T.-J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) · doi:10.1109/iccvw.2019.00343 · Cited by 203 · see citing works
Pasqualetto Cassinis, L., Fonod, R., & Gill, E. (2019) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2019.05.008 · Cited by 156 · see citing works
SPEED+: Next-Generation Dataset for Spacecraft Pose Estimation across Domain Gap
Park, T. H., Martens, M., Lecuyer, G., Izzo, D., & D'Amico, S. (2022) · 2022 IEEE Aerospace Conference (AERO) · doi:10.1109/aero53065.2022.9843439 · Cited by 170 · see citing works
Robust Model-Based Monocular Pose Initialization for Noncooperative Spacecraft Rendezvous
Sharma, S., Ventura, J., & D’Amico, S. (2018) · Journal of Spacecraft and Rockets · doi:10.2514/1.a34124 · Cited by 128 · see citing works
Wide-Depth-Range 6D Object Pose Estimation in Space
Hu, Y., Speierer, S., Jakob, W., Fua, P., & Salzmann, M. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01561 · Cited by 131 · see citing works
Pose estimation for spacecraft relative navigation using model-based algorithms
Opromolla, R., Fasano, G., Rufino, G., & Grassi, M. (2017) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2017.2650785 · Cited by 127 · see citing works
Robust multi-task learning and online refinement for spacecraft pose estimation across domain gap
Park, T. H., & D’Amico, S. (2024) · Advances in Space Research · doi:10.1016/j.asr.2023.03.036 · Cited by 125 · see citing works
“…(SPEED) (Sharma & D’Amico, 2019; Sharma et al., 2019) and the international Satellite Pose Estimation Competition (SPEC2019) (Kisantal et al., 2020) co-organized by the Advanced Concepts Team (ACT) of the European Space Agency (ESA) and the Space Rendezvous Laboratory (SLAB) at Stanford University.”
MethodHigh-Performance Embedded Computing in Space: Evaluation of Platforms for Vision-Based Navigation
Lentaris, G., Maragos, K., Stratakos, I., Papadopoulos, L., Papanikolaou, O., Soudris, D., Lourakis, M., Zabulis, X., González-Arjona, D., & Furano, G. (2018) · Journal of Aerospace Information Systems · doi:10.2514/1.i010555 · Cited by 122 · see citing works
Pauly, L., Rharbaoui, W., Shneider, C., Rathinam, A., Gaudillière, V., & Aouada, D. (2023) · Acta Astronautica · doi:10.1016/j.actaastro.2023.08.001 · Cited by 121 · see citing works
“Following this trend, the proposals of DL-based spacecraft pose estimation algorithms have outnumbered [151], [20] the classical feature-engineering-based methods [32, 148, 87, 147, 135, 19] in recent years.”
MethodStereovision-based pose and inertia estimation of unknown and uncooperative space objects
Pesce, V., Lavagna, M. R., & Bevilacqua, R. (2016) · Advances in Space Research · doi:10.1016/j.asr.2016.10.002 · Cited by 111 · see citing works
Comparative assessment of techniques for initial pose estimation using monocular vision
Sharma, S., & D׳Amico, S. (2016) · Acta Astronautica · doi:10.1016/j.actaastro.2015.12.032 · Cited by 94 · see citing works
Satellite Pose Estimation Competition 2021: Results and Analyses
Park, T. H., Märtens, M., Jawaid, M., Wang, Z., Chen, B., Chin, T.-J., Izzo, D., & D’Amico, S. (2023) · Acta Astronautica · doi:10.1016/j.actaastro.2023.01.002 · Cited by 91 · see citing works
Self-citationZhou, Y., Zhang, L., Cao, Y., & Wu, Z. (2018) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2018.2875503 · Cited by 86 · see citing works
Autonomous relative navigation around uncooperative spacecraft based on a single camera
Pesce, V., Opromolla, R., Sarno, S., Lavagna, M., & Grassi, M. (2019) · Aerospace Science and Technology · doi:10.1016/j.ast.2018.11.042 · Cited by 80 · see citing works
Optical-and-Radar Image Fusion for Dynamic Estimation of Spin Satellites
Zhou, Y., Zhang, L., Cao, Y., & Huang, Y. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2955248 · Cited by 65 · see citing works
Wang, Z., Chen, M., Guo, Y., Li, Z., & Yu, Q. (2024) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2023.3250385 · Cited by 64 · see citing works
Park, T. H., & D’Amico, S. (2023) · Journal of Guidance, Control, and Dynamics · doi:10.2514/1.g007387 · Cited by 63 · see citing works
Revisiting Monocular Satellite Pose Estimation With Transformer
Wang, Z., Zhang, Z., Sun, X., Li, Z., & Yu, Q. (2022) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2022.3161605 · Cited by 59 · see citing works
LSPnet: A 2D Localization-oriented Spacecraft Pose Estimation Neural Network
Garcia, A., Musallam, M. A., Gaudillière, V., Ghorbel, E., Ismaeil, K. A., Perez, M. D., & Aouada, D. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00233 · Cited by 53 · see citing works
Cassinis, L. P., Fónod, R., Gill, E. K. A., Ahrns, I., & Gil-Fernández, J. (2021) · Acta Astronautica · doi:10.1016/j.actaastro.2021.01.035 · Cited by 53 · see citing works
Cosmas, K., & Kenichi, A. (2020) · Aerospace · doi:10.3390/aerospace7110159 · Cited by 51 · see citing works
Space Target Attitude Estimation From ISAR Image Sequences With Key Point Extraction Network
Xie, P., Zhang, L., Du, C., Wang, X., & Zhong, W. (2021) · IEEE Signal Processing Letters · doi:10.1109/lsp.2021.3075606 · Cited by 48 · see citing works
Attitude and Size Estimation of Satellite Targets Based on ISAR Image Interpretation
Wang, J., Du, L., Li, Y., Lyu, G., & Chen, B. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3139962 · Cited by 48 · see citing works
Comparison of filtering techniques for relative attitude estimation of uncooperative space objects
Pesce, V., Haydar, M. F., Lavagna, M. R., & Lovera, M. (2018) · Aerospace Science and Technology · doi:10.1016/j.ast.2018.10.031 · Cited by 48 · see citing works
Cassinis, L. P., Menicucci, A., Gill, E. K. A., Ahrns, I., & Sánchez-Gestido, M. (2022) · Acta Astronautica · doi:10.1016/j.actaastro.2022.04.002 · Cited by 46 · see citing works
Huo, Y., Li, Z., & Zhang, F. (2020) · IEEE Access · doi:10.1109/access.2020.3041415 · Cited by 44 · see citing works
CNN-Based Pose Estimation System for Close-Proximity Operations Around Uncooperative Spacecraft
Cassinis, L. P., Fónod, R., Gill, E. K. A., Ahrns, I., & Fernandez, J. G. (2020) · AIAA SciTech Forum · doi:10.2514/6.2020-1457 · Cited by 36 · see citing works
Monocular Relative Pose Estimation Pipeline for Uncooperative Resident Space Objects
Piazza, M., Maestrini, M., & Lizia, P. D. (2022) · Journal of Aerospace Information Systems · doi:10.2514/1.i011064 · Cited by 37 · see citing works
Robust spacecraft relative pose estimation via CNN-aided line segments detection in monocular images
Bechini, M., Gu, G., Lunghi, P., & Lavagna, M. (2024) · Acta Astronautica · doi:10.1016/j.actaastro.2023.11.049 · Cited by 35 · see citing works
Wang, J., Li, Y., Song, M., & Xing, M. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3159345 · Cited by 36 · see citing works
Zhou, Y., Zhang, L., & Cao, Y. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2888631 · Cited by 33 · see citing works
Learning-Based Pose Estimation of Non-Cooperative Spacecrafts with Uncertainty Prediction
Li, K., Zhang, H., & Hu, C. (2022) · Aerospace · doi:10.3390/aerospace9100592 · Cited by 32 · see citing works
Wang, J., Li, Y., Du, L., Song, M., & Xing, M. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3101805 · Cited by 32 · see citing works
Sparse Unorganized Point Cloud Based Relative Pose Estimation for Uncooperative Space Target
Yin, F., Chou, W., Wu, Y. J., Yang, G., & Xu, S. (2018) · Sensors · doi:10.3390/s18041009 · Cited by 31 · see citing works
Design of relative trajectories for in orbit proximity operations
Opromolla, R., Fasano, G., Rufino, G., & Grassi, M. (2018) · Acta Astronautica · doi:10.1016/j.actaastro.2018.01.062 · Cited by 30 · see citing works
Beierle, C., & D’Amico, S. (2019) · Journal of Spacecraft and Rockets · doi:10.2514/1.a34337 · Cited by 29 · see citing works
Self-citationHuang, H., Zhao, G., Gu, D., & Bo, Y. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3115844 · Cited by 27 · see citing works
Vision-based Neural Scene Representations for Spacecraft
Mergy, A., Lécuyer, G., Derksen, D., & Izzo, D. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00228 · Cited by 26 · see citing works
Pose Initialization of Uncooperative Spacecraft by Template Matching with Sparse Point Cloud
Guo, W., Hu, W., Liu, C., & Lu, T. (2021) · Journal of Guidance Control and Dynamics · doi:10.2514/1.g005042 · Cited by 24 · see citing works
Opromolla, R., Vela, C., Nocerino, A., & Lombardi, C. (2022) · Remote Sensing · doi:10.3390/rs14184483 · Cited by 24 · see citing works
Stereovision-based relative states and inertia parameter estimation of noncooperative spacecraft
Wang, X., Wang, Z., & Zhang, Y. (2018) · Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering · doi:10.1177/0954410018782021 · Cited by 24 · see citing works
ChiNet: Deep recurrent convolutional learning for multimodal spacecraft pose estimation
Rondao, D., Aouf, N., & Richardson, M. A. (2022) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2022.3193085 · Cited by 24 · see citing works
Zhou, Y., Zhang, L., Wei, S., & Cao, Y. (2020) · IEEE Transactions on Computational Imaging · doi:10.1109/tci.2020.2995052 · Cited by 23 · see citing works
CA-SpaceNet: Counterfactual analysis for 6D pose estimation in space
Wang, S., Wang, S., Jiao, B., Yang, D., Su, L., Zhai, P., Chen, C., & Zhang, L. (2022) · 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981172 · Cited by 24 · see citing works
Sajjad, N., Mirshams, M., & Hein, A. M. (2024) · Astrodynamics · doi:10.1007/s42064-023-0193-1 · Cited by 20 · see citing works
Monocular Pose Estimation of an Uncooperative Spacecraft Using Convexity Defect Features
Han, H., Kim, H., & Bang, H. (2022) · Sensors · doi:10.3390/s22218541 · Cited by 18 · see citing works
Cassinis, L. P., Park, T. H., Stacey, N., D’Amico, S., Menicucci, A., Gill, E. K. A., Ahrns, I., & Sánchez-Gestido, M. (2023) · Advances in Space Research · doi:10.1016/j.asr.2023.02.021 · Cited by 19 · see citing works
Constrained Kalman filter for uncooperative spacecraft estimation by stereovision
Jiang, C., & Hu, Q. (2020) · Aerospace Science and Technology · doi:10.1016/j.ast.2020.106133 · Cited by 18 · see citing works
Liu, F., Zhang, Z., & Li, S. (2023) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2023.3332075 · Cited by 17 · see citing works
Deep-Learning-Based Direct Attitude Estimation for Uncooperative Known Space Objects
Qiao, S., Zhang, H., Xie, F., & Jiang, Z. (2023) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2023.3325801 · Cited by 19 · see citing works
A comprehensive study on PnP-based pipeline for pose estimation of noncooperative satellite
Yu, Y., Wang, Z., Li, Z., & Yu, Q. (2024) · Acta Astronautica · doi:10.1016/j.actaastro.2024.08.027 · Cited by 16 · see citing works
CNN-Based Pose Estimation of a Noncooperative Spacecraft With Symmetries From LiDAR Point Clouds
Renaut, L., Frei, H., & Nüchter, A. (2024) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2024.3517574 · Cited by 17 · see citing works
Laboratory test of pose determination algorithms for uncooperative spacecraft
Opromolla, R., Fraia, M. Z. D., Fasano, G., Rufino, G., & Grassi, M. (2017) · IEEE International Workshop on Metrology for AeroSpace (MetroAeroSpace) · doi:10.1109/metroaerospace.2017.7999558 · Cited by 16 · see citing works
Pose Tracking of a Noncooperative Spacecraft During Docking Maneuvers Using a Time-of-Flight Sensor
Ventura, J., Fleischner, A., & Walter, U. (2016) · AIAA Guidance, Navigation, and Control Conference and Exhibit · doi:10.2514/6.2016-0875 · Cited by 16 · see citing works
Space Target 3-D Reconstruction Using Votes Accumulation Method of ISAR Image Sequence
Xu, D., Wang, X., Wu, Z., Fu, J., Zhang, Y., Chen, J., & Xing, M. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3401707 · Cited by 15 · see citing works
Pose and motion estimation of unknown tumbling spacecraft using stereoscopic vision
Feng, Q., Zhu, Z. H., Pan, Q., & Liu, Y. (2018) · Advances in Space Research · doi:10.1016/j.asr.2018.04.034 · Cited by 15 · see citing works
Rapid Abstraction of Spacecraft 3D Structure from Single 2D Image
Park, T. H., & D’Amico, S. (2024) · AIAA SciTech Forum · doi:10.2514/6.2024-2768 · Cited by 14 · see citing works
Park, T., & D’Amico, S. (2023) · IEEE International Conference on Robotics and Automation · doi:10.1109/icra57147.2024.10610138 · Cited by 18 · see citing works
Chen, Z., Gui, H., & Zhong, R. (2024) · Advances in Space Research · doi:10.1016/j.asr.2024.11.006 · Cited by 14 · see citing works
Klionovska, K., & Burri, M. (2021) · Sensors · doi:10.3390/s21041455 · Cited by 14 · see citing works
Motion estimation of uncooperative space objects: A case of multi-platform fusion
Zarei‐Jalalabadi, M., & Malaek, S. M. (2018) · Advances in Space Research · doi:10.1016/j.asr.2018.07.031 · Cited by 14 · see citing works
Monocular Satellite Pose Estimation Based on Uncertainty Estimation and Self-Assessment
Wang, J., Li, Y., Li, Z., Wang, Z., & Yu, Q. (2024) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2024.3441569 · Cited by 13 · see citing works
Observability-Based Space Noncooperative Target Motion Estimation
Zhang, Y., Wang, J., Sun, B., Hou, B., Wang, D., & Zhou, X. (2024) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2024.3460756 · Cited by 13 · see citing works
Pose Estimation Method for Non-Cooperative Target Based on Deep Learning
Deng, L., Suo, H., Jia, Y., & Huang, C. (2022) · Aerospace · doi:10.3390/aerospace9120770 · Cited by 12 · see citing works
Reconstructing Satellites in 3D From Amateur Telescope Images
Chang, Z., Liu, B., Xia, Y., Guo, Y., Shi, B., & Sun, H. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3599949 · Cited by 12 · see citing works
Object-centric Reconstruction and Tracking of Dynamic Unknown Objects Using 3D Gaussian Splatting
Barad, K. R., Richard, A., Dentler, J., Olivares-Méndez, M. A., & Martínez, C. (2024) · International Conference on Space Robotics (iSpaRo) · doi:10.1109/isparo60631.2024.10688304 · Cited by 11 · see citing works
Pose Estimation of a Noncooperative Target Based on Monocular Visual SLAM
Lei, T., Liu, X., Cai, G., Liu, Y., & Liu, P. (2019) · International Journal of Aerospace Engineering · doi:10.1155/2019/9086891 · Cited by 11 · see citing works
End-to-end Monocular Pose Estimation for Uncooperative Spacecraft based on Direct Regression Network
Huang, H., Song, B., Zhao, G., & Bo, Y. (2023) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2023.3256971 · Cited by 20 · see citing works
Relative visual navigation around an unknown and uncooperative spacecraft
Barbier, T., & Gao, Y. (2023) · Acta Astronautica · doi:10.1016/j.actaastro.2023.02.022 · Cited by 10 · see citing works
Park, T. H., Bosse, J., & D'Amico, S. (2021) · arXiv · doi:10.48550/arxiv.2108.05529 · Cited by 10 · see citing works
Self-citationRobust and efficient single-CNN-based spacecraft relative pose estimation from monocular images
Bechini, M., & Lavagna, M. R. (2025) · Acta Astronautica · doi:10.1016/j.actaastro.2025.04.016 · Cited by 9 · see citing works
Close Range Tracking of an Uncooperative Target in a Sequence of Photonic Mixer Device (PMD) Images
Klionovska, K., Ventura, J., Benninghoff, H., & Huber, F. (2018) · Robotics · doi:10.3390/robotics7010005 · Cited by 9 · see citing works
Spacecraft Formation Keeping and Reconfiguration Using Optimal Visual Servoing
Pomares, J., Felicetti, L., Garcia, G. J., & Ramón, J. (2024) · The Journal of the Astronautical Sciences · doi:10.1007/s40295-024-00439-6 · Cited by 8 · see citing works
On-Ground Validation of a CNN-based Monocular Pose Estimation System for Uncooperative Spacecraft
Cassinis, L. P., Menicucci, A., Gill, E. K. A., Ahrns, I., & Fernandez, J. G. (2021) · European Conference on Space Debris · Cited by 7 · see citing works
Unsupervised Method of Infrared Spacecraft Image Foreground Extraction
Shi, J.-F., Ulrich, S., & Ruel, S. (2019) · Journal of Spacecraft and Rockets · doi:10.2514/1.a34449 · Cited by 7 · see citing works
Investigating vision transformers for bridging domain gap in satellite pose estimation
Lotti, A., Modenini, D., & Tortora, P. (2023) · Studies in Computational Intelligence · doi:10.1007/978-3-031-25755-1_20 · Cited by 7 · see citing works
Cassinis, L. P., Fónod, R., Gill, E. K. A., Ahrns, I., & Fernandez, J. G. (2019) · Cited by 7 · see citing works
Monocular SLAM with Point and Line Features Applied to Spacecraft Relative Navigation
Pan, R., Wang, C., Zhai, Z., Liu, J., Pan, T., & Chen, X. (2024) · International Journal of Aeronautical and Space Sciences · doi:10.1007/s42405-024-00817-2 · Cited by 6 · see citing works
Ye, R., Ren, Y., Zhu, X., Wang, Y., Liu, M., & Wang, L. (2023) · Remote Sensing · doi:10.3390/rs15225278 · Cited by 6 · see citing works
Yu, X., Wang, L., Gong, R., Zhang, G., & Zhu, D. (2024) · Digital Signal Processing · doi:10.1016/j.dsp.2024.104389 · Cited by 5 · see citing works
Monocular-vision-based spacecraft relative state estimation under dual number algebra
Liang, H., Wang, J., Wang, Y., & Huo, W. (2019) · Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering · doi:10.1177/0954410019864754 · Cited by 5 · see citing works
Volpe, R., Sabatini, M., Palmerini, G. B., & Mora, D. (2020) · Aerotecnica Missili & Spazio · doi:10.1007/s42496-020-00034-6 · Cited by 5 · see citing works
Jansson, O., & Harris, M. W. (2023) · Aerospace · doi:10.3390/aerospace10010071 · Cited by 4 · see citing works
Comparison of Visual-Inertial Sensor Fusion Techniques for GNSS-Resilient Navigation
Turci, L., Vitiello, F., Causa, F., Opromolla, R., & Fasano, G. (2024) · IEEE/AIAA Digital Avionics Systems Conference (DASC) · doi:10.1109/dasc62030.2024.10748659 · Cited by 4 · see citing works
Deep learning-based monocular relative pose estimation of uncooperative spacecraft
Piazza, M., Maestrini, M., & Lizia, P. D. (2021) · Virtual Community of Pathological Anatomy (University of Castilla La Mancha) · Cited by 4 · see citing works
Non-Cooperative Spacecraft Pose Estimation Using Convolutional Neural Networks
Despond, F. (2021) · doi:10.22215/etd/2021-14867 · Cited by 4 · see citing works
VIPE: Visible and Infrared Fused Pose Estimation Framework for Space Noncooperative Objects
Zhang, Z., Zhou, D. X., Hu, Y., Ma, W., Sun, G., & Zhang, Y. (2025) · Sensors · doi:10.3390/s25216664 · Cited by 3 · see citing works
Park, T. H., & D’Amico, S. (2025) · International Conference on Space Robotics (iSpaRo) · doi:10.1109/isparo66239.2025.11437016 · Cited by 4 · see citing works
Chen, J., He, Z., Wu, F., Zhao, Z., & Chen, X. (2026) · Aerospace Science and Technology · doi:10.1016/j.ast.2026.112310 · Cited by 3 · see citing works
Pose acquisition of non-cooperative spacecraft by point cloud template matching
Feng, J., Zhang, Z., Yang, Z., Yu, J., Chen, W., Chang, L., & Chen, H. (2025) · Advances in Space Research · doi:10.1016/j.asr.2025.03.035 · Cited by 3 · see citing works
Optimal Pose Estimation with Error-Covariance Analysis
Cheng, Y., & Crassidis, J. L. (2021) · AIAA SciTech Forum · doi:10.2514/6.2021-1758 · Cited by 3 · see citing works
Monocular 6-DoF Pose Estimation for Non-cooperative Spacecrafts Using Riemannian Regression Network
Chu, S., Duan, Y., Schilling, K., & Wu, S. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25056-9_13 · Cited by 4 · see citing works
Hardware/Software Co-Design of a Feature-Based Satellite Pose Estimation System
Liu, Y., Bettens, A., & Wu, X. (2023) · IEEE Journal on Miniaturization for Air and Space Systems · doi:10.1109/jmass.2023.3328879 · Cited by 3 · see citing works
Autonomous Vision-Based Navigation around Asteroids Using Convolutional Neural Networks
der Heijden, L. V., Mooij, E., & Woicke, S. (2025) · AIAA SciTech Forum · doi:10.2514/6.2025-1699 · Cited by 3 · see citing works
Scale-Consistent Learnable PnP Network for Space Target Pose Estimation
Yang, X., Wang, J., & Duan, S. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3541246 · Cited by 3 · see citing works
6-DoF Pose Estimation for Axisymmetric Objects Using Deep Learning with Uncertainty
Hashimoto, S., Hirano, D., & Ishihama, N. (2020) · IEEE Aerospace Conference (AERO) · doi:10.1109/aero47225.2020.9172298 · Cited by 3 · see citing works
- Authors
- D', S., Amico, N. A., Benn, M., & Jørgensen, J. L.
- Affiliations
- Stanford University (US)
- DTU Space (DK)
- Venue
- International Journal of Space Science and Engineering
- Publisher
- Inderscience Publishers
- Published
- 2014
- Volume
- 2
- Issue
- 2
- Pages
- 171
- ISSN
- 2048-8459 (ISSN-L)
- 2048-8467
- OpenAlex
- W2594388292
- Cited by
- 132
- References
- 16
- Citations by year
- 2026: 10 · 2025: 13 · 2024: 21 · 2023: 20 · 2022: 12 · 2021: 17 · 2020: 10 · 2019: 10 · 2018: 11 · 2017: 3 · 2016: 5
- FWCI
- 3.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Space Science and Engineering (publishedVersion)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Space Satellite Systems and Control
- Physical Sciences › Engineering › Aerospace Engineering › Spacecraft Dynamics and Control
- Physical Sciences › Engineering › Ocean Engineering › Satellite Image Processing and Photogrammetry
- Keywords
- pose estimation, on-orbit servicing, formation flying, perceptual organization, perspective projection, RANSAC, PRISMA mission, spaceborne imaging
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