3D terrain feature matching based navigation for lunar soft landing

Ping Yuan Cui; Jun Hua Feng; Sheng Ying Zhu; Hu Tao Cui

doi:10.3873/j.issn.1000-1328.2011.03.003

3D terrain feature matching based navigation for lunar soft landing

Ping Yuan Cui^*
, Jun Hua Feng
, Sheng Ying Zhu
, Hu Tao Cui

^*Corresponding author for this work

School of Aerospace Engineering

Harbin Institute of Technology
Xi'an Satellite Control Center

Research output: Contribution to journal › Article › peer-review

14 Citations (Scopus)

Abstract

A 3D terrain feature matching based autonomous navigation method for lunar pinpoint landing is presented in this paper. Firstly, 3D terrain features are detected by searching local maximum of the digital elevation map's local variance. Secondly, based on the invariants of relative distance and angle, a vote method is used to match detected terrain features with the feature database of global digital elevation map. Thirdly, for uncertainties of measurement noise statistics in pose estimation, the Sage-Husa noise statistics estimator is fused with an iterative Kalman Filter to provide accurate estimation of spacecraft position and attitude. Simulation results are given to show feasibility of the proposed navigation scheme.

Original language	English
Pages (from-to)	470-476
Number of pages	7
Journal	Yuhang Xuebao/Journal of Astronautics
Volume	32
Issue number	3
DOIs	https://doi.org/10.3873/j.issn.1000-1328.2011.03.003
Publication status	Published - Mar 2011

Keywords

Lunar soft landing
Optical navigation
Pose estimation
Terrain matching

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10.3873/j.issn.1000-1328.2011.03.003

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title = "3D terrain feature matching based navigation for lunar soft landing",

abstract = "A 3D terrain feature matching based autonomous navigation method for lunar pinpoint landing is presented in this paper. Firstly, 3D terrain features are detected by searching local maximum of the digital elevation map's local variance. Secondly, based on the invariants of relative distance and angle, a vote method is used to match detected terrain features with the feature database of global digital elevation map. Thirdly, for uncertainties of measurement noise statistics in pose estimation, the Sage-Husa noise statistics estimator is fused with an iterative Kalman Filter to provide accurate estimation of spacecraft position and attitude. Simulation results are given to show feasibility of the proposed navigation scheme.",

keywords = "Lunar soft landing, Optical navigation, Pose estimation, Terrain matching",

author = "Cui, \{Ping Yuan\} and Feng, \{Jun Hua\} and Zhu, \{Sheng Ying\} and Cui, \{Hu Tao\}",

year = "2011",

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doi = "10.3873/j.issn.1000-1328.2011.03.003",

language = "English",

volume = "32",

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AU - Cui, Ping Yuan

AU - Feng, Jun Hua

AU - Zhu, Sheng Ying

AU - Cui, Hu Tao

PY - 2011/3

Y1 - 2011/3

N2 - A 3D terrain feature matching based autonomous navigation method for lunar pinpoint landing is presented in this paper. Firstly, 3D terrain features are detected by searching local maximum of the digital elevation map's local variance. Secondly, based on the invariants of relative distance and angle, a vote method is used to match detected terrain features with the feature database of global digital elevation map. Thirdly, for uncertainties of measurement noise statistics in pose estimation, the Sage-Husa noise statistics estimator is fused with an iterative Kalman Filter to provide accurate estimation of spacecraft position and attitude. Simulation results are given to show feasibility of the proposed navigation scheme.

AB - A 3D terrain feature matching based autonomous navigation method for lunar pinpoint landing is presented in this paper. Firstly, 3D terrain features are detected by searching local maximum of the digital elevation map's local variance. Secondly, based on the invariants of relative distance and angle, a vote method is used to match detected terrain features with the feature database of global digital elevation map. Thirdly, for uncertainties of measurement noise statistics in pose estimation, the Sage-Husa noise statistics estimator is fused with an iterative Kalman Filter to provide accurate estimation of spacecraft position and attitude. Simulation results are given to show feasibility of the proposed navigation scheme.

KW - Lunar soft landing

KW - Optical navigation

KW - Pose estimation

KW - Terrain matching

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JO - Yuhang Xuebao/Journal of Astronautics

JF - Yuhang Xuebao/Journal of Astronautics

IS - 3

ER -

3D terrain feature matching based navigation for lunar soft landing

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Keywords

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