Guastella DC, Muscato G. Learning-Based Methods of Perception and Navigation for Ground Vehicles in Unstructured Environments: A Review.
Sensors (Basel) 2020;
21:s21010073. [PMID:
33375609 PMCID:
PMC7795560 DOI:
10.3390/s21010073]
[Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 12/19/2020] [Accepted: 12/21/2020] [Indexed: 11/30/2022]
Abstract
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.
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