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CNN Based Detectors on Planetary Environments: A Performance Evaluation.

Federico Furlán1, Elsa Rubio1, Humberto Sossa1

  • 1Instituto Politécnico Nacional, Centro de Investigación en Computación, Ciudad de México, México.

Frontiers in Neurorobotics
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PubMed
Summary

This study introduces a new method for autonomous exploration robots to detect rocks on Mars-like surfaces using a modified Single-Shot-Detector (SSD) convolutional neural network. This approach enhances navigation capabilities in remote planetary environments.

Keywords:
convolutional neural network (CNN)machine learningplanetary explorationremote sensingrock detection

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Area of Science:

  • Robotics
  • Computer Vision
  • Planetary Science

Background:

  • Autonomous exploration robots require robust environmental perception for navigation in remote or challenging terrains.
  • Effective rock detection is crucial for robotic missions on planets like Mars, aiding in navigation and scientific analysis.
  • Existing methods often rely on classification or handcrafted features, limiting their adaptability and performance.

Purpose of the Study:

  • To propose and evaluate a novel algorithm for detecting rocks in planetary surface images.
  • To leverage deep learning, specifically convolutional neural networks, for enhanced rock detection capabilities.
  • To offer an alternative methodology to traditional rock classification techniques in planetary exploration.

Main Methods:

  • Implementation of a modified Single-Shot-Detector (SSD) network architecture.
  • Training and evaluation of the SSD model on datasets simulating Martian environments.
  • Focus on object detection rather than mere image classification for rock identification.

Main Results:

  • The proposed SSD-based methodology demonstrates effective rock detection in simulated Martian conditions.
  • The modified SSD architecture provides a viable alternative to existing rock detection and classification methods.
  • The system's performance was evaluated, showcasing its potential for robotic navigation.

Conclusions:

  • The developed convolutional neural network-based rock detection algorithm is a promising advancement for autonomous planetary exploration.
  • This approach offers improved environmental perception for robots operating in Mars-like terrains.
  • The study contributes a new, data-driven methodology for rock detection, moving beyond traditional feature engineering.