A Flexible New Technique for Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Logistic Regression, AdaBoost and Bregman Distances
Machine Learning
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
Learning a Classification Model for Segmentation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
ACM SIGGRAPH 2004 Papers
ACM SIGGRAPH 2005 Papers
Recovering Surface Layout from an Image
International Journal of Computer Vision
A survey of motion-parallax-based 3-D reconstruction algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Robotics and Autonomous Systems
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Obstacle avoidance is a key component of autonomous systems. In particular, when dealing with large robots in unmodified environments, robust obstacle avoidance is vital. This paper presents a new image-based exploration algorithm for a mobile robot equipped only with a monocular pan-tilt camera to autonomously explore the natural scene structure in indoor environments. The algorithm inferred and computed the frontier information directly from the segmentation images and classified each super-pixel as belong either to an obstacle or the ground plane. The method used the distance and orientation information of the frontier to control the robot to avoid collisions. Experimental results on a mobile robot in an unmodified laboratory and corridor environments demonstrate the validity of the approach.