Real-time visual pose estimation: from BOP objects to custom drone - A journey

被引:0
作者
Rey, Thomas [1 ]
Moras, Julien [1 ]
Eudes, Alexandre [1 ]
Manzanera, Antoine [2 ]
机构
[1] Univ Paris Saclay, DTIS, ONERA, 6 Chemin Vauve Granges, F-91120 Palaiseau, France
[2] Inst Polytech Paris, ENSTA, U2IS, 828 Blvd Marechaux, F-91120 Palaiseau, France
关键词
Drone swarms; Far range; Pose estimation; Monocular; Computer Vision;
D O I
10.1016/j.mechatronics.2025.103339
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Pose estimation plays a crucial role in robotics for prehension tasks or in augmented-reality application, yet its application on real-world far-range estimation has not been thoroughly studied. This study aims to evaluate pose estimators on a custom drone at distances from 0.5 m to 10 m, which is beyond the scope of existing datasets, that only contain objects close to less than 2 m. We created synthetic and real databases specific to our drone and compared various RGB pose estimators, evaluating their performance across different distances. PViT-6D, being one of the SoTA methods on the classic [0,2] m interval, also outperforms others estimators at greater distances, and proves robust with respect to detection inaccuracy. The results demonstrate the potential of PViT-6D to be used on a real time application embedded in the drone platform. This work aims to evaluate the potential of pose estimators for mutual perception and communication within a drone swarm.
引用
收藏
页数:11
相关论文
共 34 条
[1]  
Brachmann E, 2014, LECT NOTES COMPUT SC, V8690, P536, DOI 10.1007/978-3-319-10605-2_35
[2]   Deep Regression on Manifolds: A 3D Rotation Case Study [J].
Bregier, Romain .
2021 INTERNATIONAL CONFERENCE ON 3D VISION (3DV 2021), 2021, :166-174
[3]  
Claessens L, 2023, BMVC, P543
[4]  
Csehi AI, 2023, Arxiv, DOI [arXiv:2310.03563, 10.48550/ARXIV.2310.03563]
[5]  
Denninger M., 2023, Journal of Open Source Software, V8, P4901, DOI [DOI 10.21105/JOSS.04901, 10.21105/joss.04901]
[6]   Region-Based Convolutional Networks for Accurate Object Detection and Segmentation [J].
Girshick, Ross ;
Donahue, Jeff ;
Darrell, Trevor ;
Malik, Jitendra .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2016, 38 (01) :142-158
[7]   Deep Residual Learning for Image Recognition [J].
He, Kaiming ;
Zhang, Xiangyu ;
Ren, Shaoqing ;
Sun, Jian .
2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2016, :770-778
[8]   BOP Challenge 2020 on 6D Object Localization [J].
Hodan, Tomas ;
Sundermeyer, Martin ;
Drost, Bertram ;
Labbe, Yann ;
Brachmann, Eric ;
Michel, Frank ;
Rother, Carsten ;
Matas, Jiri .
COMPUTER VISION - ECCV 2020 WORKSHOPS, PT II, 2020, 12536 :577-594
[9]   BOP: Benchmark for 6D Object Pose Estimation [J].
Hodan, Tomas ;
Michel, Frank ;
Brachmann, Eric ;
Kehl, Wadim ;
Buch, Anders Glent ;
Kraft, Dirk ;
Drost, Bertram ;
Vidal, Joel ;
Ihrke, Stephan ;
Zabulis, Xenophon ;
Sahin, Caner ;
Manhardt, Fabian ;
Tombari, Federico ;
Kim, Tae-Kyun ;
Matas, Jiri ;
Rother, Carsten .
COMPUTER VISION - ECCV 2018, PT X, 2018, 11214 :19-35
[10]   A Comprehensive Review on 3D Object Detection and 6D Pose Estimation With Deep Learning [J].
Hoque, Sabera ;
Arafat, Md. Yasir ;
Xu, Shuxiang ;
Maiti, Ananda ;
Wei, Yuchen .
IEEE ACCESS, 2021, 9 :143746-143770