文献类型：期刊文献

中文题名：Computer vision tasks for intelligent aerospace perception: An overview

英文题名：Computer vision tasks for intelligent aerospace perception: An overview

作者：Chen, Huilin[1];Sun, Qiyu[1];Li, Fangfei[2];Tang, Yang[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Sch Math, Shanghai 200237, Peoples R China

年份：2024

卷号：67

期号：9

起止页码：2727

中文期刊名：Science China(Technological Sciences)

外文期刊名：SCIENCE CHINA-TECHNOLOGICAL SCIENCES

收录：CSTPCD;;EI(收录号：20240304677);Scopus;WOS:【SCI-EXPANDED(收录号:WOS:001297594500001)】；CSCD:【CSCD2023_2024】；PubMed;

基金：This work was supported by the National Natural Science Foundation of China (Grant Nos. 62233005 and 62293502), the Programme of Introducing Talents of Discipline to Universities (the 111 Project) (Grant No. B17017), the Fundamental Research Funds for the Central Universities (Grant No.222202417006), and Shanghai AI Lab.

语种：英文

中文关键词：deep learning;perception;aerospace missions;pose estimation;3D reconstruction;recognition

外文关键词：deep learning; perception; aerospace missions; pose estimation; 3D reconstruction; recognition

摘要：Computer vision tasks are crucial for aerospace missions as they help spacecraft to understand and interpret the space environment,such as estimating position and orientation, reconstructing 3D models, and recognizing objects, which have been extensively studied to successfully carry out the missions. However, traditional methods like Kalman filtering, structure from motion, and multi-view stereo are not robust enough to handle harsh conditions, leading to unreliable results. In recent years, deep learning(DL)-based perception technologies have shown great potential and outperformed traditional methods, especially in terms of their robustness to changing environments. To further advance DL-based aerospace perception, various frameworks, datasets,and strategies have been proposed, indicating significant potential for future applications. In this survey, we aim to explore the promising techniques used in perception tasks and emphasize the importance of DL-based aerospace perception. We begin by providing an overview of aerospace perception, including classical space programs developed in recent years, commonly used sensors, and traditional perception methods. Subsequently, we delve into three fundamental perception tasks in aerospace missions: pose estimation, 3D reconstruction, and recognition, as they are basic and crucial for subsequent decision-making and control. Finally, we discuss the limitations and possibilities in current research and provide an outlook on future developments,including the challenges of working with limited datasets, the need for improved algorithms, and the potential benefits of multisource information fusion.
Computer vision tasks are crucial for aerospace missions as they help spacecraft to understand and interpret the space environment, such as estimating position and orientation, reconstructing 3D models, and recognizing objects, which have been extensively studied to successfully carry out the missions. However, traditional methods like Kalman filtering, structure from motion, and multi-view stereo are not robust enough to handle harsh conditions, leading to unreliable results. In recent years, deep learning (DL)-based perception technologies have shown great potential and outperformed traditional methods, especially in terms of their robustness to changing environments. To further advance DL-based aerospace perception, various frameworks, datasets, and strategies have been proposed, indicating significant potential for future applications. In this survey, we aim to explore the promising techniques used in perception tasks and emphasize the importance of DL-based aerospace perception. We begin by providing an overview of aerospace perception, including classical space programs developed in recent years, commonly used sensors, and traditional perception methods. Subsequently, we delve into three fundamental perception tasks in aerospace missions: pose estimation, 3D reconstruction, and recognition, as they are basic and crucial for subsequent decision-making and control. Finally, we discuss the limitations and possibilities in current research and provide an outlook on future developments, including the challenges of working with limited datasets, the need for improved algorithms, and the potential benefits of multi-source information fusion.