Review of Visual Navigation Technology Based on Craters

被引:0
|
作者
Xu Liheng [1 ,2 ]
Jiang Jie [1 ,2 ]
Ma Yan [1 ,2 ]
机构
[1] Beihang Univ, Sch Instrumentat & Optoelect Engn, Beijing 100191, Peoples R China
[2] Beihang Univ, Key Lab Precis Optomech Technol, Minist Educ, Beijing 100191, Peoples R China
关键词
fiber optics and optical communication; visual modeling; algorithms; detection; pattern recognition; MARTIAN IMPACT CRATERS; CONVOLUTIONAL NEURAL-NETWORK; DETECTION ALGORITHMS; AUTONOMOUS NAVIGATION; OPTICAL NAVIGATION; PLANETARY IMAGES; LUNAR; RECOGNITION; EXPLORATION; TOPOGRAPHY;
D O I
10.3788/LOP223406
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Autonomous navigation is a key technology for deep space exploration, and several autonomous landing missions on extraterrestrial planets have been executed by China and other nations. Autonomous visual navigation technology based on craters is a current research hotspot. Numerous planets have rich crater features, and pose estimation based on terrain features is an important technology for visual navigation. This work first briefly introduces the recent application progress of navigation technology in the field of deep space exploration and the classification of autonomous navigation methods. Visual navigation has been classified according to sensor imaging, focusing on the terrain relative navigation method based on craters. Subsequently, the advantages and difficulties of the crater-based method are summarized, definition and data types of craters are introduced, and domestic and foreign research institutions and personnel have been presented. Moreover, the navigation method based on craters is divided into three stages, crater detection, crater recognition, and pose calculation, and the research advances in crater detection methods, from supervised detection, to unsupervised detection, and finally to composite detection, are introduced thoroughly. The work introduces domestic and foreign methods of crater recognition according to the stage and the presence or absence of initial attitude information, respectively, and then introduces the pose calculation method based on image information and the method combined with dynamic models, respectively. Finally, craterbased visual navigation technology is summarized, and prospects for its development are discussed.
引用
收藏
页数:21
相关论文
共 118 条
  • [1] Christian JA, 2020, Arxiv, DOI arXiv:2009.01228
  • [2] Automated crater shape retrieval using weakly-supervised deep learning
    Ali-Dib, Mohamad
    Menou, Kristen
    Jackson, Alan P.
    Zhu, Chenchong
    Hammond, Noah
    [J]. ICARUS, 2020, 345
  • [3] Analysis of Camera Pose Estimation Using 2D Scene Features for Augmented Reality Applications
    Alsadat, Shabnam Meshkat
    Laurendeau, Denis
    [J]. IMAGE AND SIGNAL PROCESSING (ICISP 2018), 2018, 10884 : 243 - 251
  • [4] Andersson L. E., 1982, NASA Catalogue of Lunar Nomenclature
  • [5] [Anonymous], 2008, AIAA GUIDANCE NAVIGA
  • [6] LEAST-SQUARES FITTING OF 2 3-D POINT SETS
    ARUN, KS
    HUANG, TS
    BLOSTEIN, SD
    [J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1987, 9 (05) : 699 - 700
  • [7] Impact crater recognition on mars based on a probability volume created by template matching
    Bandeira, Lourenco
    Saraiva, Jose
    Pina, Pedro
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2007, 45 (12): : 4008 - 4015
  • [8] Detection of sub-kilometer craters in high resolution planetary images using shape and texture features
    Bandeira, Lourenco
    Ding, Wei
    Stepinski, Tomasz F.
    [J]. ADVANCES IN SPACE RESEARCH, 2012, 49 (01) : 64 - 74
  • [9] Barata T, 2004, LECT NOTES COMPUT SC, V3212, P489
  • [10] Campbell T, 2022, DEEP LEARNING APPROA