CAJ | 학술논문

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。
제출기우층급실현기억( HTM)망락적지도창건방법。해방법이용층급실시기억장제도문제등효위장경식별문제，배경지도유일계렬HTM모형수출적장경구성。수선종획취도상중제취위치불변로봉특정( PIRF)。병이용PIRF구건시각사회표，근거사회표장도상적PIRF묘술부영사위시각단사빈솔시량。다개시각단사빈솔시량구성적서렬수입HTM망락，용우실현배경지도적학습여창건급배로장경적추단식별。채용량조실험수거험증문중방법，결과표명기우HTM적제도책략능성공건립배경지도，병능고효처리배로검측문제。
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.