Abstract
The capabilities that Artificial Intelligence and Computer Vision can provide to intelligent robotic systems is well recognized and as a result it is the subject of topical research in recent years. This paper will provide a broad review of the progress which has been made in applying deep learning and vision sensor data for the autonomous navigation of unmanned ground vehicles (UGVs). The current state-of-the-art techniques are compared in terms of their performance, implementation and deployment and performance. An outline of some of the most popular types of computer vision techniques is provided, as well as insights into how the recent availability of 3D vision systems can be exploited in the domain.
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