Horizon Line Estimation In Glacial Environments Using Multiple Visual Cues

• Published in: 2011 IEEE International Conference on Robotics and Automation
• Main Authors: Williams, Stephen, Howard, Ayanna M.
• Other Authors: Georgia Institute of Technology. Human-Automation Systems Lab, Georgia Institute of Technology. School of Electrical and Computer Engineering, Georgia Institute of Technology. Center for Robotics and Intelligent Machines
• Format: Conference Object
• Language: English
• Published: Georgia Institute of Technology 2011
• Subjects: Robotics; Glacial environments; Arctic; glaciers; Alaska
• Online Access: http://hdl.handle.net/1853/38614; https://doi.org/10.1109/ICRA.2011.5980006

Presented at the IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China, 9-13 May 2011. ©2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. DOI:10.1109/ICRA.2011.5980006 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.