【摘要】 在概率的框架内,基于主元神经网络,提出了一种新的蒙特卡罗道路跟踪技术,用于自主陆地车辆在非结构化道路上的导航.使用直线道路模型表示道路边缘,并对其状态利用二阶自回归模型进行预测;在HSV彩色空间将颜色信息和局部空间特征相结合,利用主元神经网络提取主成分;根据道路边缘窗的统计特性,利用粒子滤波器进行道路状态的估计.实验结果表明,该方法能够鲁棒地进行非结构化道路跟踪.更多还原

【Abstract】 Within a probabilistic framework, based on principal component analysis neural networks, a novel Monte Carlo tracking technique is suggested for autonomous navigation of land vehicles on unstructured roads. The straight road model is used to represent road edges and its status is predicted by the second-order autoregressive model. Color information and local spatial features are combined in the HSV color space in order to obtain principal components by principal component analysis neural networks. The status of the road is estimated by particle filters according to statistical features of road edge windows. Experimental results show that the unstructured road tracking can be robustly realized by the method.更多还原