Intelligent vision-guided trajectory reconstruction enables rapid localization and characterization of the Chang’E-6 landing site
Year: 2025
Type: article
Abstract: The Chang’E-6 (CE-6) mission’s first lunar farside samples return advances understanding of lunar evolution. This achievement highlights the necessity of rapid landing-trajectory reconstruction and precise landing-site characterization for mission execution. We present an intelligent vision-guided framework for lunar exploration, demonstrating that high-fidelity trajectory reconstruction can simultaneously enhance landing site localization accuracy and geological analysis precision. Our approach integrates: high-precision trajectory reconstruction from descent imagery, rapid localization utilizing deep learning-enhanced landmark matching achieving 0.90 m landing-site accuracy for Chang’E-6, and physically constrained geological analysis with visual rectification. By correcting camera distortions via pose parameters, we improve crater morphometry and surface age dating through orthorectified CSFD measurements. Our results reveal late-stage volcanism ( ~ 855 Ma) in Apollo crater ejecta, challenging early farside magmatic cessation models. Furthermore, we identify pristine anorthosite-pyroxene assemblages critical for probing lunar mantle differentiation. This work establishes an intelligent vision-guided approach to computational photogrammetry for extraterrestrial sample-return missions. A high-fidelity reconstruction of the Chang’E-6 landing trajectory based on descent imagery can rapidly and accurately locate the landing site and be used for geological analyses including crater morphometry and regolith mineralogy.
Authors: Shihao Shu, Liqun Lin, Bingxu Hou, Fangyi Cheng, Dingquan Xue, Chenfeng Hou, Yong Lai, Cai Meng, Lu Li, Yan Li, Xiangzhi Bai
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,490
Awards: G1614956369, G6880456256, G7578578277
OpenAlex ID: W4417429700
