摘要
针对同步定位与地图构建(simultaneous localization and mapping,SLAM)过程中定位精度较低和角度漂移等问题,受哺乳动物海马体空间认知机理的启发,提出一种构建多尺度网格细胞到位置细胞信息转换的仿生SLAM算法.首先,引入头方向细胞和条纹细胞感知自身运动信息,并生成多尺度网格细胞覆盖整个空间环境,减小由于角度偏移而产生的累计误差;其次,对于定位精度低问题,采用Hebb学习规则下的竞争型神经网络建立多尺度网格细胞到位置细胞的信息转换关系;最后,构建位置细胞与空间环境中不同地标的映射关系,通过选取最大放电率的位置细胞形成空间认知拓扑地图,实现移动机器人的自主定位.与RatSLAM和ORB-SLAM2在KITTI公开数据集上进行对比实验,结果表明,所提算法能够通过对位置信息进行编码实现未知环境中的自主定位和建图,同时,控制平移误差不超过1.50 m,旋转误差不高于1.0°.
Aiming at the problems of low positioning accuracy and angle drift in the process of simultaneous localization and mapping(SLAM),inspired by the spatial cognitive mechanism of mammalian hippocampus,a bionic SLAM algorithm for constructing information conversion from multi-scale grid cell to place cell is proposed.Firstly,the proposed algorithm introduces head direction cell and stripe cell to perceive their own motion information while generating a multi-scale grid cell to cover the entire spatial environment,which can reduce the cumulative error due to angular offset.Secondly,as for the problem of low localization accuracy,the proposed algorithm uses a competitive neural network under Hebb learning rule to establish the information conversion relationship from multi-scale grid cell to place cell.Meanwhile,the mapping relationship between place cell and different landmarks in the spatial environment is constructed.Finally,the place cells with the maximum discharge rate are selected in order to form spatial cognitive topological map while realizing the autonomous localization of mobile robots.Compared with RatSLAM and ORB-SLAM2 on the KITTI public dataset,the results show that the proposed algorithm can realize autonomous localization and mapping in unknown environments by encoding the location information,while controlling the translation error at no more than 1.50 m and the rotation error at no higher than 1.0°.
作者
陈孟元
田德红
Chen Mengyuan;Tian Dehong(School of Electrical Engineering,Anhui Polytechnic University,Wuhu 241000;Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment,Ministry of Education,Wuhu 241000)
出处
《计算机辅助设计与图形学学报》
EI
CSCD
北大核心
2021年第5期712-723,共12页
Journal of Computer-Aided Design & Computer Graphics
基金
国家自然科学基金(61903002)
安徽省自然科学基金(1808085QF215)
安徽省重点研究与开发计划(1804b06020375)
芜湖市科技计划(2020yf59)
安徽工程大学中青年拔尖人才项目.
关键词
认知地图
路径整合
位置细胞
网格细胞
定位精度
cognitive map
path integration
place cell
grid cell
positioning accuracy
