Horizon line estimation in glacial environments using multiple visual cues

While the arctic possesses significant information of scientific value, surprisingly little work has focused on developing robotic systems to collect this data. For arctic robotic data collection to be a viable solution, a method for navigating in the arctic, and thus of assessing glacial terrain, must be developed. Segmenting the ground plane from the rest of the image is one common aspect of a visual hazard detection system. However, the properties of glacial images, namely low contrast, overcast sky, and cloud, mountain, and snow sharing common colors, pose difficulties for most visual algorithms. A horizon line detection scheme is presented which uses multiple visual cues to rank candidate horizon segments, then constructs a horizon line consistent with those cues. Weak cues serve to reinforce a selected path, while strong cues have the ability to redirect it. Further, the system infers the horizon location in areas that are visually ambiguous. The performance of the proposed system has been tested on multiple data sets collected on two different glaciers in Alaska, and compares favorably, both in terms of time and classification performance, to representative segmentation algorithms from several different classes.