基于贝叶斯核主成分分析的远距离地形标记方法

由于非结构化室外场景外观特征分布存在动态不确定性以及映射偏移特性,因此在室外移动机器人自主导航的过程中采用预确定外观特征并不能非常有效地进行地形标记.为了解决此问题,提出了基于贝叶斯核主成分分析(BKPCA)的远距离地形标记方法.该方法融合了基于贝叶斯公式的聚类中心后验概率,且采用自定义的核函数,实现了原始特征数据结构在低维空间上的保持,能够提取出适合当前场景地形标记的外观特征.实验结果表明,BKPCA模型有效地提高了远距离地形标记的精度.

Long-distance Terrain Labeling Based on Bayesian Kernel Principal Component Analysis

When adopting predetermined appearance features in autonomous navigation of an outdoor mobile robot,terrain labeling may not be effectively done because of the dynamic uncertainty and the mapping deviation of appearance feature distribution in unstructured outdoor scenes.To solve this problem,a long-distance terrain labeling method based on Bayesian kernel principal component analysis(BKPCA) is proposed.The method incorporates the posterior probability of cluster centers using Bayesian formula and employs a self-defined kernel function.The method can implement the maintenance of the original data structure in the low dimensional space and can extract suitable appearance features for current scene labeling. The experimental result validates that the BKPCA model improves accuracy of long-distance terrain labeling.