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Author:

Li Jiangeng (Li Jiangeng.) | Tang Haibo (Tang Haibo.) | Ruan Xiaogang (Ruan Xiaogang.) | Wei Ruoyan (Wei Ruoyan.) | Jia Zhen (Jia Zhen.)

Indexed by:

CPCI-S

Abstract:

Optical navigation technology is widely used in precision guidance for autonomous landing and obstacle avoidance. According to the requirements of the computer vision landing location system on a planetary lander, this paper mainly studies the image feature matching algorithm in the process of autonomous navigation during landing on asteroids. A matching algorithm based on local feature regions was proposed, which takes the invariant moment and mutual information as similarty measures in the two phases of feature matching, and introduces bidirectional matching strategy, to improve matching accuracy. In order to solve the problems of the feature matching for zoom image, this paper put forward an improved SIFT algorithm, the experimental results showed that this method can not only match feature points effectively for zoom image, but also achieve high matching accuracy and is qualified for engineering application.

Keyword:

regional mutual information SIFT feature matching autonomous navigation

Author Community:

  • [ 1 ] [Li Jiangeng]Beijing Univ Technol, Coll Elect Informat & Control Engn, Beijing 100124, Peoples R China
  • [ 2 ] [Tang Haibo]Beijing Univ Technol, Coll Elect Informat & Control Engn, Beijing 100124, Peoples R China
  • [ 3 ] [Ruan Xiaogang]Beijing Univ Technol, Coll Elect Informat & Control Engn, Beijing 100124, Peoples R China
  • [ 4 ] [Wei Ruoyan]Beijing Univ Technol, Coll Elect Informat & Control Engn, Beijing 100124, Peoples R China
  • [ 5 ] [Jia Zhen]Beijing Univ Technol, Coll Elect Informat & Control Engn, Beijing 100124, Peoples R China

Reprint Author's Address:

  • [Li Jiangeng]Beijing Univ Technol, Coll Elect Informat & Control Engn, Beijing 100124, Peoples R China

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Source :

26TH CHINESE CONTROL AND DECISION CONFERENCE (2014 CCDC)

ISSN: 1948-9439

Year: 2014

Page: 5287-5291

Language: English

Cited Count:

WoS CC Cited Count: 1

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

Affiliated Colleges:

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