Abstract

The boundaries of objects in an image are often considered a nuisance to be "handled" due to the occlusion they exhibit. Since most, if not all, computer vision techniques aggregate information spatially within a scene, information spanning these boundaries, and therefore from different physical surfaces, is invariably and erroneously considered together. In addition, these boundaries convey important perceptual information about 3D scene structure and shape. Consequently, their identification can benefit many different computer vision pursuits, from low-level processing techniques to high-level reasoning tasks.While much focus in computer vision is placed on the processing of individual, static images, many applications actually offer video, or sequences of images, as input. The extra temporal dimension of the data allows the motion of the camera or the scene to be used in processing. In this paper, we focus on the exploitation of subtle relative-motion cues present at occlusion boundaries. When combined with more standard appearance information, we demonstrate these cues' utility in detecting occlusion boundaries locally. We also present a novel, mid-level model for reasoning more globally about object boundaries and propagating such local information to extract improved, extended boundaries.

Increasing the level of spacecraft autonomy is essential for broadening the reach of solar system exploration. Computer vision has and will continue to play an important role in increasing autonomy of both spacecraft and Earthbased robotic vehicles. This article addresses progress on computer vision for planetary rovers and landers and has four main parts. First, we review major milestones in the development of computer vision for robotic vehicles over the last four decades. Since research on applications for Earth and space has often been closely intertwined, the review includes elements of both. Second, we summarize the design and performance of computer vision algorithms used on Mars in the NASA/JPL Mars Exploration Rover (MER) mission, which was a major step forward in the use of computer vision in space. These algorithms did stereo vision and visual odometry for rover navigation and feature tracking for horizontal velocity estimation for the landers. Third, we summarize ongoing research to improve vision systems for planetary rovers, which includes various aspects of noise reduction, FPGA implementation, and vision-based slip perception. Finally, we briefly survey other opportunities for computer vision to impact rovers, landers, and orbiters in future solar system exploration missions.

Abstract

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