基于层级实时记忆的地图创建方法  

Map Building Method Based on Hierarchical Temporal Memory

作　　者：张新征[1] 麦晓春[2] 张建芬[1] 

机构地区：[1]暨南大学电气信息学院,珠海519070 [2]香港中文大学电子工程学系,香港999077

出　　处：《模式识别与人工智能》2015年第4期316-326,共11页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.61203338)资助

摘　　要：提出基于层级实现记忆(HTM)网络的地图创建方法.该方法利用层级实时记忆将制图问题等效为场景识别问题,环境地图由一系列HTM模型输出的场景构成.首先从获取图像中提取位置不变鲁棒特征(PIRF).并利用PIRF构建视觉词汇表,根据词汇表将图像的PIRF描述符映射为视觉单词频率矢量.多个视觉单词频率矢量构成的序列输入HTM网络,用于实现环境地图的学习与创建及环路场景的推断识别.采用两组实验数据验证文中方法,结果表明基于HTM的制图策略能成功建立环境地图,并能高效处理环路检测问题.A map building method based on hierarchical temporal memory （ HTM） is proposed. The mapping problem is treated as scene recognition. The map is composed of a series of scenes being the outputs of HTM network. Firstly, the position invariant robust feature （ PIRF） is extracted from the obtained images and then the PIRFs are applied to build the visual vocabulary. Secondly, according to the visual vocabulary PIRF descriptors of an image are projected to the vector of visual word occurrences. Multiple visual word occurrences vectors are formed as a sequence of visual word occurrences. This sequence is inputted to HTM to implement the environment map learning and building and closed loop scenes recognition. The performance of the proposed mapping method is evaluated by two experiments. The results show that the proposed strategy based on HTM is effective for map building and closed loop detection.

关 键 词：地图创建 层级实时记忆(HTM) 位置不变鲁棒特征(PIRF) 视觉词汇 大脑皮层学习算法(CLA) Hierarchical Temporal Memory(HTM) Position Invariant Robust Feature(PIRF) Cortex Learning Algorithm(CLA) 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]