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Semantic Terrain Segmentation in the Navigation Vision of Planetary Rovers-A Systematic Literature Review.

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This systematic literature review analyzes semantic terrain segmentation for planetary rovers. While AI advances show promise, current solutions lack pixel-level accuracy, real-time performance, and onboard hardware compatibility, with no suitable open datasets available.

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Area of Science:

  • Planetary Science
  • Computer Vision
  • Artificial Intelligence

Background:

  • Visual semantic segmentation is crucial for planetary rover autonomy, aiding localization, perception, and path planning.
  • Advances in AI and computer vision offer new opportunities for rover navigation and exploration.
  • A systematic literature review (SLR) is essential to analyze current solutions, identify data availability, and pinpoint research gaps.

Approach:

  • A rigorous SLR was conducted, screening 320 studies from IEEE Xplore, Web of Science, and Scopus (up to May 2022).
  • Studies focused on semantic terrain segmentation for planetary rover navigation vision.
  • 30 papers were included after four screening rounds, applying robust inclusion/exclusion criteria and quality assessment.

Key Points:

  • Included studies cover navigation (16), geological analysis (7), and exploration efficiency (10).
  • Analysis includes distributions of time, study type, geography, publisher, and experimental settings.
  • Key research questions evaluate current achievements and future research gaps.

Conclusions:

  • AI and computer vision have significantly improved accuracy, data availability, and real-time performance in semantic segmentation.
  • No current solution meets all requirements: pixel-level segmentation, real-time inference, and onboard hardware compatibility.
  • An open, pixel-level annotated, real-world dataset for rover navigation is still lacking.