Teleoperation User Interfaces for Mining Robotics
Autonomous Robots
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Autonomous system for navigation and surveying in underground mines: Field Reports
Journal of Field Robotics - Special Issue on Mining Robotics
A practical approach to robotic design for the DARPA Urban Challenge
Journal of Field Robotics - Special Issue on the 2007 DARPA Urban Challenge, Part I
IEEE Transactions on Robotics
ETFA'09 Proceedings of the 14th IEEE international conference on Emerging technologies & factory automation
Region merging techniques using information theory statistical measures
IEEE Transactions on Image Processing
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The mining industry is constantly faced with the dual needs for safety and improved productivity. It is widely recognized that robots can play a significant role in pre-disaster (pre-emption) and post-disaster (recovery) mine rescue operations. This would inevitably enhance productivity and greatly reduce human exposure to dangerous underground mine environment. Nonetheless, the success of a robot in a mine depends greatly on its visual capability to correctly interpret its immediate environment for navigational purposes. This work serves to assist robots' drivability in an underground mine. A probabilistic approach based on the local entropy is employed. The entropy is measured within a fixed window on a stream of mine frames to compute features used in the segmentation process. We then compare results using the statistical region merging (SRM) approach and evaluate the performance to provide useful qualitative and quantitative conclusions. Different regions of the mine, such as the shaft, stope and gallery, are investigated and results show that a good drivable region can be detected in an underground mine environment.