A survey on recent object detection techniques useful for monocular vision-based planetary terrain classification

• The aim of this paper is to bridge the gap between monocular vision-based terrain classification and object detection in computer vision, by presenting a broad and structured overview of recent computer vision techniques behind the successes of object detection.
• We believe that these techniques provide great potentials for terrain classification using monocular vision processing.

Direct terrain classification from monocular images for autonomous navigation of planetary rovers is a relatively new and challenging research area, not only because of the hardware limitation of a rover, but also because the rocks and obstacles to be detected exhibit diverse morphologies and have no uniform properties to distinguish them from background soil. We present a survey of recently developed object detection techniques that can be useful for terrain classification for planetary rovers. We start with summarizing current vision-based terrain classification methods. We then provide a comprehensive and structured overview of recent object detection techniques, focusing on those applicable to terrain classification.