Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Robot Vision
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Techniques for Autonomous, Off-Road Navigation
IEEE Intelligent Systems
Color models for outdoor machine vision
Computer Vision and Image Understanding
Ladar-Based Discrimination of Grass from Obstacles for Autonomous Navigation
ISER '00 Experimental Robotics VII
On Perpendicular Texture: Why do we see more flowers in the distance?
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
A Specialized Multibaseline Stereo Technique for Obstacle Detection
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Visual perception of obstacles and vehicles for platooning
IEEE Transactions on Intelligent Transportation Systems
Toward Reliable Off Road Autonomous Vehicles Operating in Challenging Environments
International Journal of Robotics Research
Learning Outdoor Color Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Bayes fusion of visual features
Image and Vision Computing
Reverse Optical Flow for Self-Supervised Adaptive Autonomous Robot Navigation
International Journal of Computer Vision
Data Structures for Efficient Dynamic Processing in 3-D
International Journal of Robotics Research
Autonomous geocaching: navigation and goal finding in outdoor domains
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Visual detection of novel terrain via two-class classification
Proceedings of the 2009 ACM symposium on Applied Computing
A multi-range vision strategy for autonomous offroad navigation
RA '07 Proceedings of the 13th IASTED International Conference on Robotics and Applications
Obstacle Detection, Identification and Sharing on a Robotic Soccer Team
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Saliency-Based Obstacle Detection and Ground-Plane Estimation for Off-Road Vehicles
ICVS '09 Proceedings of the 7th International Conference on Computer Vision Systems: Computer Vision Systems
Scene Categorization by Introducing Contextual Information to the Visual Words
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Adaptive Bayesian filtering for vibration-based terrain classification
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Traversable path identification in unstructured terrains: a Markov random walk approach
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Online, self-supervised vision-based terrain classification in unstructured environments
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Scene categorization via contextual visual words
Pattern Recognition
ICVR'07 Proceedings of the 2nd international conference on Virtual reality
Terrain-based sensor selection for autonomous trail following
RobVis'08 Proceedings of the 2nd international conference on Robot vision
Digital elevation map estimation by vision-lidar fusion
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Non-contact terrain classification for autonomous mobile robot
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Neural Network Based Terrain Classification Using Wavelet Features
Journal of Intelligent and Robotic Systems
General road detection from a single image
IEEE Transactions on Image Processing
Learning from Demonstration for Autonomous Navigation in Complex Unstructured Terrain
International Journal of Robotics Research
Hybrid simulation of Sensor and Actor Networks with BARAKA
Wireless Networks
Information Sciences: an International Journal
Robotics and Autonomous Systems
Robotics and Autonomous Systems
Self-learning classification of radar features for scene understanding
Robotics and Autonomous Systems
Terrain classification and identification of tree stems using ground-based LiDAR
Journal of Field Robotics
Terrain traversability analysis methods for unmanned ground vehicles: A survey
Engineering Applications of Artificial Intelligence
Probabilistic terrain classification in unstructured environments
Robotics and Autonomous Systems
Three-dimensional point cloud plane segmentation in both structured and unstructured environments
Robotics and Autonomous Systems
Comparison of different approaches to visual terrain classification for outdoor mobile robots
Pattern Recognition Letters
Journal of Intelligent and Robotic Systems
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Autonomous navigation in cross-country environments presents many new challenges with respect to more traditional, urban environments. The lack of highly structured components in the scene complicates the design of even basic functionalities such as obstacle detection. In addition to the geometric description of the scene, terrain typing is also an important component of the perceptual system. Recognizing the different classes of terrain and obstacles enables the path planner to choose the most efficient route toward the desired goal.This paper presents new sensor processing algorithms that are suitable for cross-country autonomous navigation. We consider two sensor systems that complement each other in an ideal sensor suite: a color stereo camera, and a single axis ladar. We propose an obstacle detection technique, based on stereo range measurements, that does not rely on typical structural assumption on the scene (such as the presence of a visible ground plane); a color-based classification system to label the detected obstacles according to a set of terrain classes; and an algorithm for the analysis of ladar data that allows one to discriminate between grass and obstacles (such as tree trunks or rocks), even when such obstacles are partially hidden in the grass. These algorithms have been developed and implemented by the Jet Propulsion Laboratory (JPL) as part of its involvement in a number of projects sponsored by the US Department of Defense, and have enabled safe autonomous navigation in high-vegetated, off-road terrain.