Semantic terrain segmentation in the navigation vision of planetary rovers – a systematic literature review

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dc.contributor.authorKuang, Boyu
dc.contributor.authorGu, Chengzhen
dc.contributor.authorRana, Zeeshan A.
dc.contributor.authorZhao, Yifan
dc.contributor.authorSun, Shuang
dc.contributor.authorNnabuife, Somtochukwu Godfrey
dc.date.accessioned2022-11-02T15:17:15Z
dc.date.available2022-11-02T15:17:15Z
dc.date.issued2022-11-01
dc.description.abstractBackground: 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. Methods: 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. Results: 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. Conclusions: 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. Contributions: 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.en_UK
dc.identifier.citationKuang B, Gu C, Rana ZA, et al., (2022) Semantic terrain segmentation in the navigation vision of planetary rovers – a systematic literature review, Sensors, Volume 22, Issue 21, November 2022, Article number 8393en_UK
dc.identifier.issn1424-8220
dc.identifier.urihttps://doi.org/10.3390/s22218393
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/18644
dc.language.isoenen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectrover autonomyen_UK
dc.subjectvisual localizationen_UK
dc.subjectopen dataseten_UK
dc.subjectimage processingen_UK
dc.subjectterrain annotationen_UK
dc.titleSemantic terrain segmentation in the navigation vision of planetary rovers – a systematic literature reviewen_UK
dc.typeArticleen_UK

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