深空探测学报（中英文）2025，Vol.12Issue(2)：172-178,7.DOI:10.15982/j.issn.2096-9287.2025.20240044
隐式3D表征学习的星表障碍物检测方法
Implicit 3D Representation Learning for Extraterrestrial Obstacle Detection
摘要
Abstract
Based on traditional image-based obstacle detection methods can only locate obstacles in 2D image plane,requiring additional measurement methods such as stereo matching to obtain depth information and then determine the 3D positions of obstacles.However,stereo matching faces challenges of high computational cost and decreased accuracy when dealing with complex environments.Therefore,we propose an implicit 3D representation learning method for extraterrestrial obstacle detection was proposed.It encodes the potential three-dimensional coordinates of each point into image features,and the generated features can effectively establish an implicit conversion from 2D images to 3D space,thereby enabling direct prediction of the 3D positions of obstacles.Experiments conducted on Mars surface images collected by the Spirit rover demonstrate that the proposed method can effectively identify locations and sizes of obstacles,achieving 85.5%average precision.The proposed method in this study presents an innovative framework for planetary surface obstacle detection,with substantial potential to advance autonomous navigation capabilities in lunar/Martian exploration rovers.关键词
地外星表障碍物检测/3D位置编码/3D目标检测Key words
extraterrestrial obstacle detection/3D position embedding/3D object detection分类
信息技术与安全科学引用本文复制引用
杨文飞,姜涵,潘晓扬,李茂登,周晔,张天柱..隐式3D表征学习的星表障碍物检测方法[J].深空探测学报（中英文）,2025,12(2):172-178,7.基金项目
国防基础科学研究(JCKY2021130B016) （JCKY2021130B016）
