摘要
自主导航是深空探测的关键技术,国内外已经进行了多项地外行星自主着陆任务。基于陨石坑进行自主视觉导航的技术是当前的研究热点,各类行星的陨石坑特征丰富,基于地形特征进行位姿估计是视觉导航的重要技术。本文首先简要介绍了近几年深空探测领域导航技术的应用进展以及自主导航方法的分类,并根据传感器成像方式对视觉导航进行了分类,重点介绍了基于陨石坑的地形相对导航方法。然后总结了基于陨石坑方法的优势和难点,介绍了陨石坑的定义与数据类型,概括了国内外研究机构和人员。接着将基于陨石坑的导航方法分为陨石坑检测、陨石坑识别和位姿解算三个阶段,分别从监督检测、非监督检测和复合检测等方面详细介绍了陨石坑检测方法的研究现状,同时根据着陆阶段和有无初始姿态信息分别介绍了陨石坑识别的国内外方法,随之详细介绍了基于图像信息和结合动力学模型的位姿解算方法。最后对基于陨石坑的视觉导航技术进行了总结,并对其发展前景进行了展望。
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.
作者
许利恒
江洁
马岩
Xu Liheng;Jiang Jie;Ma Yan(School of Instrumentation and Optoelectronic Engineering,Beihang University,Beijing 100191,China;Key Laboratory of Precision Opto-Mechatronics Technology,Ministry of Education,Beihang University,Beijing 100191,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2023年第11期172-192,共21页
Laser & Optoelectronics Progress
基金
国家自然科学基金(61725501)。
关键词
光纤光学与光通信
视觉建模
算法
检测
模式识别
fiber optics and optical communication
visual modeling
algorithms
detection
pattern recognition