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

The problem of autonomous navigation of a ground vehicle in unstructured environments is both challenging and crucial for the deployment of this type of vehicle in real-world applications. Several well-established communities in robotics research deal with these scenarios such as search and rescue robotics, planetary exploration, and agricultural robotics. Perception plays a crucial role in this context, since it provides the necessary information to make the vehicle aware of its own status and its surrounding environment. We present a review on the recent contributions in the robotics literature adopting learning-based methods to solve the problem of environment perception and interpretation with the final aim of the autonomous context-aware navigation of ground vehicles in unstructured environments. To the best of our knowledge, this is the first work providing such a review in this context.

Automated summary

The review focuses on recent contributions in robotics research that use learning-based methods to address the issue of autonomous perception and interpretation for ground vehicles in unstructured environments. Perception is highlighted as crucial for autonomous navigation, providing necessary information for accurate navigation in complex environments. This review is the first of its kind in this context, shedding light on the importance of perception for context-aware navigation.