Vision for Mobile Robot Navigation: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Real-Time Stereo Vision for Mobile Robot Navigation
Autonomous Robots
Appearance-Based Obstacle Detection with Monocular Color Vision
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
High Speed Road Boundary Detection with CNN-Based Dynamic Programming
PCM '02 Proceedings of the Third IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Road lane segmentation using dynamic programming for active safety vehicles
Pattern Recognition Letters
Segmenting Humans from Mobile Thermal Infrared Imagery
IWINAC '09 Proceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part II: Bioinspired Applications in Artificial and Natural Computation
Online, self-supervised vision-based terrain classification in unstructured environments
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
General road detection from a single image
IEEE Transactions on Image Processing
Optical flow or image subtraction in human detection from infrared camera on mobile robot
Robotics and Autonomous Systems
Variational method for super-resolution optical flow
Signal Processing
Tracking-based semi-supervised learning
International Journal of Robotics Research
Road scene segmentation from a single image
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
Tracking natural trails with swarm-based visual saliency
Journal of Field Robotics
Neural-swarm visual saliency for path following
Applied Soft Computing
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Autonomous mobile robot navigation, either off-road or on ill-structured roads, presents unique challenges for machine perception. A successful terrain or roadway classifier must be able to learn in a self-supervised manner and adapt to inter- and intra-run changes in the local environment.This paper demonstrates the improvements achieved by augmenting an existing self-supervised image segmentation procedure with an additional supervisory input. Obstacles and roads may differ in appearance at distance because of illumination and texture frequency properties. Reverse optical flow is added as an input to the image segmentation technique to find examples of a region of interest at previous times in the past. This provides representations of this region at multiple scales and allows the robot to better determine where more examples of this class appear in the image.