Abstract
Autonomous driving is a challenging problem, particularly when the domain is unstructured, as in an outdoor agricultural setting. Thus, advanced perception systems are primarily required to sense and understand the surrounding environment recognizing artificial and natural structures, topology, vegetation and paths. In this paper, a self-learning framework is proposed to automatically train a ground classifier for scene interpretation and autonomous navigation based on multi-baseline stereovision. The use of rich 3D data is emphasized where the sensor output includes range and color information of the surrounding environment. Two distinct classifiers are presented, one based on geometric data that can detect the broad class of ground and one based on color data that can further segment ground into subclasses. The geometry-based classifier features two main stages: an adaptive training stage and a classification stage. During the training stage, the system automatically learns to associate geometric appearance of 3D stereo-generated data with class labels. Then, it makes predictions based on past observations. It serves as well to provide training labels to the color-based classifier. Once trained, the color-based classifier is able to recognize similar terrain classes in stereo imagery. The system is continuously updated online using the latest stereo readings, thus making it feasible for long range and long duration navigation, over changing environments. Experimental results, obtained with a tractor test platform operating in a rural environment, are presented to validate this approach, showing an average classification precision and recall of 91.0% and 77.3%, respectively.
Highlights
Tractors are used for a variety of agricultural operations, including tilling, planting, weeding, fertilizing, spraying, hauling, mowing, and harvesting
This paper presents new sensor processing algorithms that are suitable for outdoor autonomous navigation
The stereovision-based classifier leads to the following main advantages: (a) self-training of the classifier, where the stereo camera allows the vehicle to automatically acquire a set of ground samples, eliminating the need for time-consuming manual labeling, (b) continuous updating of the system during the vehicle’s operation, making it adaptive and feasible for long range and long duration navigation applications, (c) extension of the short-range stereo classification results to long-range via segmentation of the entire visual image
Summary
Tractors are used for a variety of agricultural operations, including tilling, planting, weeding, fertilizing, spraying, hauling, mowing, and harvesting. Such versatility makes tractors prime targets for automation in order to improve productivity and efficiency, while preserving at the same time safe operations. Autonomous navigation in agricultural environments presents many challenges [1], due to the lack of highly structured elements in the scene that complicates the design of even basic functionalities. In addition to the geometric description of the scene, terrain typing is an important component of the perception system. The ability to automatically recognize obstacles and different terrain classes would result in an enabling technology for autonomous navigation systems. Vehicles that can drive autonomously in outdoor environments have received increasing interest in recent years
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