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Visual SLAM-Based Robotic Mapping Method for Planetary Construction.

Sungchul Hong1, Antyanta Bangunharcana2, Jae-Min Park1,3

  • 1Department of Geoinformatic Engineering, Inha University, Incheon 22212, Korea.

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|November 27, 2021
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Summary
This summary is machine-generated.

This study presents a new robotic mapping method using visual simultaneous localization and mapping (SLAM) for planetary exploration. It enhances 3D terrain mapping resolution for future base construction on the Moon and Mars.

Keywords:
3D terrain mapdeep learningexploration roverplanetary construction mappingvisual SLAM

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Area of Science:

  • Planetary Science
  • Robotics
  • Computer Vision

Background:

  • Recent discoveries of lunar and Martian resources like water-ice and lava tubes drive interest in in-situ resource utilization (ISRU) for long-term space exploration.
  • Current 3D terrain mapping methods for planetary surfaces, primarily based on orbiter imagery, lack the necessary resolution for construction purposes.
  • Developing high-resolution, localized 3D maps is crucial for robotic and human base construction in extraterrestrial environments.

Purpose of the Study:

  • To introduce a novel visual simultaneous localization and mapping (SLAM)-based robotic mapping method for planetary rovers.
  • To improve the resolution and accuracy of 3D terrain mapping for extraterrestrial construction applications.
  • To address the limitations of current mapping techniques in homogeneous and unstructured planetary terrains.

Main Methods:

  • The proposed method utilizes a stereo camera system mounted on a rover for visual SLAM.
  • The S-PTAM (Small-Planet Augmented Tracking and Mapping) framework is employed as the core SLAM algorithm.
  • Self-supervised deep learning is integrated to generate enhanced disparity maps, improving mapping in challenging environments.

Main Results:

  • The visual SLAM method demonstrates enhanced mapping capabilities in simulated planetary terrain conditions.
  • The integration of self-supervised deep learning significantly improves the generation of accurate disparity maps.
  • The developed technique provides higher resolution 3D terrain maps compared to traditional orbiter-based methods.

Conclusions:

  • The proposed visual SLAM-based robotic mapping method offers a viable solution for high-resolution 3D terrain mapping on planetary surfaces.
  • This advancement is critical for enabling effective base construction and long-term human exploration on the Moon and Mars.
  • The validated performance in emulated environments suggests strong potential for real-world application in future planetary missions.