Learning-Based Methods of Perception and Navigation for Ground Vehicles in Unstructured Environments: A Review

Area of Science:

• Robotics
• Artificial Intelligence
• Computer Vision

Background:

• Autonomous navigation in unstructured environments is critical for real-world robotic applications.
• Perception is essential for ground vehicles to understand their status and surroundings.
• Robotics research communities like search and rescue, planetary exploration, and agriculture face these challenges.

Purpose of the Study:

• To review recent advancements in learning-based methods for environment perception and interpretation.
• To focus on enabling autonomous, context-aware navigation for ground vehicles in unstructured environments.
• To provide the first comprehensive review of this specific research area.

Main Methods:

• Systematic literature review of robotics research.
• Focus on learning-based approaches for environment perception.
• Analysis of methods contributing to context-aware navigation.

Main Results:

• Identified a growing body of work utilizing machine learning for robotic perception.
• Highlighted the importance of perception for successful autonomous navigation in complex terrains.
• Demonstrated the potential of learning-based methods to improve situational awareness.

Conclusions:

• Learning-based perception is key to advancing autonomous navigation in unstructured environments.
• This review synthesizes current research, identifying trends and gaps.
• Future work should continue to integrate advanced perception techniques for robust robotic systems.