ALVINN: an autonomous land vehicle in a neural network
Advances in neural information processing systems 1
An autonomous guided vehicle for cargo handling applications
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Vision for Mobile Robot Navigation: A Survey
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Pattern Classification (2nd Edition)
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Stereo-Based Tree Traversability Analysis for Autonomous Off-Road Navigation
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Towards reliable perception for unmanned ground vehicles in challenging conditions
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
The Marulan Data Sets: Multi-sensor Perception in a Natural Environment with Challenging Conditions
International Journal of Robotics Research
Learning methods for generic object recognition with invariance to pose and lighting
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Short-range radar perception in outdoor environments
TAROS'11 Proceedings of the 12th Annual conference on Towards autonomous robotic systems
Radar-based perception for autonomous outdoor vehicles
Journal of Field Robotics
Beyond accuracy, f-score and ROC: a family of discriminant measures for performance evaluation
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Robotics and Autonomous Systems
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Autonomous driving is a challenging problem in mobile robotics, particularly when the domain is unstructured, as in an outdoor setting. In addition, field scenarios are often characterized by low visibility as well, due to changes in lighting conditions, weather phenomena including fog, rain, snow and hail, or the presence of dust clouds and smoke. Thus, advanced perception systems are primarily required for an off-road robot to sense and understand its environment recognizing artificial and natural structures, topology, vegetation and paths, while ensuring, at the same time, robustness under compromised visibility. In this paper the use of millimeter-wave radar is proposed as a possible solution for all-weather off-road perception. A self-learning approach is developed to train a classifier for radar image interpretation and autonomous navigation. The proposed classifier features two main stages: an adaptive training stage and a classification stage. During the training stage, the system automatically learns to associate the appearance of radar data with class labels. Then, it makes predictions based on past observations. The training set is continuously updated online using the latest radar readings, thus making it feasible to use the system for long range and long duration navigation, over changing environments. Experimental results, obtained with an unmanned ground vehicle operating in a rural environment, are presented to validate this approach. A quantitative comparison with laser data is also included showing good range accuracy and mapping ability as well. Finally, conclusions are drawn on the utility of millimeter-wave radar as a robotic sensor for persistent and accurate perception in natural scenarios.