Articles

UAV visual positioning method based on factor graph

  • Hongfa WAN ,
  • Shanshan LI ,
  • Chaozhen LAN ,
  • Mingzhi XIANG
Expand
  • School of Surveying and Mapping,Information Engineering University,Zhengzhou  450001,China

Received date: 2022-06-01

  Revised date: 2022-06-16

  Accepted date: 2022-07-25

  Online published: 2022-08-17

Supported by

National Natural Science Foundation of China(42174007)

Abstract

At present, multi-purpose UAVs mainly rely on the combined navigation of Global Navigation Satellite System(GNSS) and Inertial Measurement Unit(IMU) to achieve high-precision positioning. However, in the cases of strong electromagnetic interference and GNSS rejection in various complex environments, this positioning mode faces the risk of failure. To explore a navigation and positioning method in the GNSS rejection environment,the visual navigation method is studied. To solve the problems of poor intersection accuracy in the elevation direction, lack of absolute scale information, inability to eliminate accumulated errors, and discontinuous positioning trajectory in visual positioning under the condition of small intersection angle of UAV, a bundle adjustment method based on factor graph fusion image matching and visual information is proposed. The problem of autonomous absolute positioning under the small intersection angle of UAV is solved.

Cite this article

Hongfa WAN , Shanshan LI , Chaozhen LAN , Mingzhi XIANG . UAV visual positioning method based on factor graph[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023 , 44(S1) : 727627 -727627 . DOI: 10.7527/S1000-6893.2022.27627

References

1 NISTER D, NARODITSKY O, BERGEN J. Visual odometry[C]∥ Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2004: I.
2 GAO X, ZHANG T, LIU Y, et al. 14 lectures on visual SLAM: From theory to practice[M]. Springer Singapore,2021.
3 秦剑琪,蓝朝桢,崔志祥,等.一种面向无人机绝对定位的卫星基准影像检索方法[J].武汉大学学报(信息科学版)202348(3):368-376.
  QIN J Q, LAN C Z, CUI Z X,et al.A satellite reference image retrieval method for unmanned aerial vehicle absolute positioning[J].Geomatics and Information Science of Wuhan University202348(3):368-376 (in Chinese).
4 蓝朝桢, 卢万杰, 于君明, 等. 异源遥感影像特征匹配的深度学习算法[J]. 测绘学报202150(2): 189-202.
  LAN C Z, LU W J, YU J M, et al. Deep learning algorithm for feature matching of cross modality remote sensing images[J]. Acta Geodaetica et Cartographica Sinica202150(2): 189-202 (in Chinese).
5 夏凌楠, 张波, 王营冠, 等. 基于惯性传感器和视觉里程计的机器人定位[J]. 仪器仪表学报201334(1): 166-172.
  XIA L N, ZHANG B, WANG Y G, et al. Robot localization algorithm based on inertial sensor and video odometry[J]. Chinese Journal of Scientific Instrument201334(1): 166-172 (in Chinese).
6 李宇波, 朱效洲, 卢惠民, 等. 视觉里程计技术综述[J]. 计算机应用研究201229(8): 2801-2805.
  LI Y B, ZHU X Z, LU H M, et al. Review on visual odometry technology[J]. Application Research of Computers201229(8): 2801-2805 (in Chinese).
7 曾庆喜, 邱文旗, 冯玉朋, 等. GNSS/VO组合导航研究现状及发展趋势[J]. 导航定位学报20186(2): 1-6.
  ZENG Q X, QIU W Q, FENG Y P, et al. Status and development trend analysis of GNSS/VO integrated navigation system[J]. Journal of Navigation and Positioning20186(2): 1-6 (in Chinese).
8 赵洋, 刘国良, 田国会, 等. 基于深度学习的视觉SLAM综述[J]. 机器人201739(6): 889-896.
  ZHAO Y, LIU G L, TIAN G H, et al. A survey of visual SLAM based on deep learning[J]. Robot201739(6): 889-896 (in Chinese).
9 张祖勋, 张剑清, 廖明生, 等. 遥感影像的高精度自动配准[J]. 武汉测绘科技大学学报199823(4):320-323.
  ZHANG Z X, ZHANG J Q, LIAO M S, et al. Automatic precision registration of multi resolution remote sensing imagery[J]. Journal of Wuhan Technical University of Surveying and Mapping (Wtusm)199823(4): 320-323 (in Chinese).
10 KAESS M, RANGANATHAN A, DELLAERT F. iSAM: Incremental smoothing and mapping[J].IEEE Transactions on Robotics200824(6): 1365-1378.
11 DELLAERT F, KAESS M. Square root SAM: Simultaneous localization and mapping via square root information smoothing[J].International Journal of Robotics Research200625(12): 1181-1203.
12 张彦珍. 基于g2o的SLAM后端优化算法研究[D]. 西安: 西安电子科技大学, 2014.
  ZHANG Y Z. A study of SLAM back-end optimization algorithm based on g2o[D]. Xi’an: Xidian University, 2014 (in Chinese).
13 TRIGGS B, MCLAUCHLAN P F, HARTLEY R I, et al. Bundle adjustment:a modern synthesis[M]∥Vision algorithms: Theory and practice. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000: 298-372.
14 LUONG Q T, FAUGERAS O D. The fundamental matrix: theory, algorithms, and stability analysis[J].International Journal of Computer Vision199617(1): 43-75.
15 TORR P H S, ZISSERMAN A. Feature based methods for structure and motion estimation[M]∥Vision algorithms: Theory and practice. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000: 278-294.
16 LEPETIT V, MORENO-NOGUER F, FUA P. EPnP: an accurate On) solution to the PnP problem[J]. International Journal of Computer Vision200981(2): 155-166.
17 RUBLEE E, RABAUD V, KONOLIGE K, et al. ORB: an efficient alternative to SIFT or SURF[C]∥2011 International Conference on Computer Vision. Piscataway: IEEE Press, 2012: 2564-2571.
18 NG P C, HENIKOFF S. SIFT: predicting amino acid changes that affect protein function[J]. Nucleic Acids Research200331(13): 3812-3814.
19 ROSTEN E, PORTER R, DRUMMOND T. Faster and better: A machine learning approach to corner detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence201032(1): 105-119.
20 SARLIN P E, DETONE D, MALISIEWICZ T, et al. SuperGlue: Learning feature matching with graph neural networks[C]∥2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2020: 4937-4946.
21 DETONE D, MALISIEWICZ T, RABINOVICH A. SuperPoint: Self-supervised interest point detection and description[C]∥ 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Piscataway: IEEE Press, 2018: 337-33712.
22 TIAN Y R, FAN B, WU F C. L2-net: Deep learning of discriminative patch descriptor in euclidean space[C]∥ 2017 IEEE Conference on Computer Vision and Pattern Recognition.Piscataway:IEEE Press,2017:6128-6136.
23 YI K M, TRULLS E, LEPETIT V, et al. LIFT: Learned invariant feature transform[C]∥European Conference on Computer Vision. Cham: Springer, 2016: 467-483.
24 DAVIS T A, GILBERT J R, LARIMORE S I, et al. A column approximate minimum degree ordering algorithm[J]. ACM Transactions on Mathematical Software200430(3): 353-376.
25 吕瑞, 陈龙, 翁雪, 等. 利用先验点图模型的SLAM后端优化算法[J]. 武汉大学学报(信息科学版)201439(6): 745-749.
  LV R, CHEN L, WENG X, et al. A back-end optimization algorithm of SLAM based on graph model with prior points[J]. Geomatics and Information Science of Wuhan University201439(6): 745-749 (in Chinese).
Outlines

/