Centre for Computational Engineering Sciences (CES), Cranfield University, Cranfield MK43 0AL, UK.
Supply Chain Research Centre, Cranfield School of Management, Cranfield University, Cranfield MK43 0AL, UK.
Sensors (Basel). 2022 Nov 1;22(21):8393. doi: 10.3390/s22218393.
: The planetary rover is an essential platform for planetary exploration. Visual semantic segmentation is significant in the localization, perception, and path planning of the rover autonomy. Recent advances in computer vision and artificial intelligence brought about new opportunities. A systematic literature review (SLR) can help analyze existing solutions, discover available data, and identify potential gaps. : A rigorous SLR has been conducted, and papers are selected from three databases (IEEE Xplore, Web of Science, and Scopus) from the start of records to May 2022. The 320 candidate studies were found by searching with keywords and bool operators, and they address the semantic terrain segmentation in the navigation vision of planetary rovers. Finally, after four rounds of screening, 30 papers were included with robust inclusion and exclusion criteria as well as quality assessment. : 30 studies were included for the review, and sub-research areas include navigation (16 studies), geological analysis (7 studies), exploration efficiency (10 studies), and others (3 studies) (overlaps exist). Five distributions are extendedly depicted (time, study type, geographical location, publisher, and experimental setting), which analyzes the included study from the view of community interests, development status, and reimplementation ability. One key research question and six sub-research questions are discussed to evaluate the current achievements and future gaps. : Many promising achievements in accuracy, available data, and real-time performance have been promoted by computer vision and artificial intelligence. However, a solution that satisfies pixel-level segmentation, real-time inference time, and onboard hardware does not exist, and an open, pixel-level annotated, and the real-world data-based dataset is not found. As planetary exploration projects progress worldwide, more promising studies will be proposed, and deep learning will bring more opportunities and contributions to future studies. : This SLR identifies future gaps and challenges by proposing a methodical, replicable, and transparent survey, which is the first review (also the first SLR) for semantic terrain segmentation in the navigation vision of planetary rovers.
: 行星漫游车是行星探测的重要平台。视觉语义分割在漫游车自主定位、感知和路径规划中具有重要意义。计算机视觉和人工智能的最新进展带来了新的机遇。系统文献综述 (SLR) 可以帮助分析现有解决方案、发现可用数据并确定潜在差距。: 进行了严格的 SLR，从记录开始到 2022 年 5 月，从三个数据库 (IEEE Xplore、Web of Science 和 Scopus) 中选择论文。通过使用关键字和 bool 运算符搜索，找到了 320 篇候选研究论文，这些论文涉及行星漫游车导航视觉中的语义地形分割。最后，经过四轮筛选，纳入了 30 篇论文，这些论文具有严格的纳入和排除标准以及质量评估。: 30 项研究被纳入综述，子研究领域包括导航 (16 项研究)、地质分析 (7 项研究)、勘探效率 (10 项研究) 和其他 (3 项研究) (存在重叠)。扩展描绘了五个分布 (时间、研究类型、地理位置、出版商和实验设置)，从社区利益、发展状况和可重新实现能力的角度分析了所纳入的研究。讨论了一个关键研究问题和六个子研究问题，以评估当前的成就和未来的差距。: 计算机视觉和人工智能在提高精度、可用数据和实时性能方面取得了许多有前途的成就。然而，不存在满足像素级分割、实时推理时间和板载硬件要求的解决方案，也没有找到开放的、像素级注释的、基于真实世界的数据的数据集。随着全球行星探测项目的推进，将会有更多有前途的研究提出，而深度学习将为未来的研究带来更多的机遇和贡献。: 本 SLR 通过提出一种系统的、可复制的和透明的调查方法，确定了未来的差距和挑战，这是第一个针对行星漫游车导航视觉中的语义地形分割的综述(也是第一个 SLR)。
