Towards Autonomous Agriculture: Automatic Ground Detection Using Trinocular Stereovision
Towards Autonomous Agriculture: Automatic Ground Detection Using Trinocular Stereovision
复制标题
DOI：
10.3390/s120912405
复制
发表时间：
2012-09-12
期刊：
影响因子：
--
通讯作者：
Milella A
中科院分区：
文献类型：
--
作者：
Reina G;Milella A
自动驾驶是一个具有挑战性的问题，特别是当领域是非结构化的，比如在户外农业环境中。因此，高级感知系统主要需要感知和理解周围环境，识别人工和自然结构，拓扑结构，植被和路径。本文提出了一种基于多基线立体视觉的地面分类器自动训练框架，用于场景判读和自主导航。强调使用丰富的3D数据，其中传感器输出包括周围环境的范围和颜色信息。提出了两种不同的分类器，一种是基于几何数据的分类器，可以检测地面的大类，另一种是基于颜色数据的分类器，可以进一步将地面分割成子类。基于几何的分类器主要分为两个阶段：自适应训练阶段和分类阶段。在训练阶段，系统自动学习将三维立体生成数据的几何外观与类标签相关联。然后，它根据过去的观察做出预测。它还可以为基于颜色的分类器提供训练标签。经过训练后，基于颜色的分类器能够识别立体图像中相似的地形类别。该系统使用最新的立体声读数不断在线更新，从而使其能够在不断变化的环境中进行长距离和长时间的导航。在农村环境下的拖拉机测试平台上进行的实验结果验证了该方法的有效性，平均分类精度和召回率分别为91.0%和77.3%。
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.
登录
查看更多内容
影响因子：
4.3
作者：
Reina, Giulio;Milella, Annalisa;Underwood, James
通讯作者：
Underwood, James
影响因子：
8.3
作者：
Rovira-Mas, Francisco;Zhang, Qin;Reid, John F.
通讯作者：
Reid, John F.
影响因子：
3.5
作者：
Gennery, DB
通讯作者：
Gennery, DB
影响因子：
0.8
作者：
Neath, AA;Cavanaugh, JE
通讯作者：
Cavanaugh, JE
影响因子：
2
作者：
Rohmer, Eric;Reina, Giulio;Yoshida, Kazuya
通讯作者：
Yoshida, Kazuya