仿动物大脑网格细胞神经定位机制的同步定位与地图构建方法研究

项目名称： 仿动物大脑网格细胞神经定位机制的同步定位与地图构建方法研究

项目编号： No.61503362

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 自动化技术、计算机技术

项目作者： 余彪

作者单位： 中国科学院合肥物质科学研究院

项目金额： 18万元

中文摘要： 自然界中，动物大脑经过亿万年进化形成了多种精巧的自主导航定位机制以适应各种复杂环境。生物与导航定位领域已分别提出众多神经定位模型和仿生定位方法，可在一定程度上描述和模仿大脑神经定位机制，但这些方法仍存在精度低、拓展性差、难以适应大范围环境以及与动物实际定位机理不符等问题。针对上述问题，本项目拟模仿动物大脑网格细胞神经定位机制，建立一种新的同步定位与地图构建（SLAM）方法：以立体视觉信息处理为基础，通过探索改进网格细胞连续吸引子网络模型和振荡干扰模型，建立适宜于大范围环境、符合动物神经定位机制的路径整合方法；建模海马体位置细胞及其与网格细胞、视觉特征的关联，通过视觉与路径整合信息融合消除累积误差，实现认知地图的构建与表达；开发模型参数优化方法，提高模型的精度与扩展性。项目预期实现一种新型适宜于大范围环境的仿生SLAM方法，为自主定位提供新的技术基础，同时也为生物导航定位提供大范围环境验证。

中文关键词： 同步定位与地图构建；网格细胞；位置细胞；连续吸引子网络模型；振荡干扰模型

英文摘要： In natural world, animal’s brain has evolved considerable neural localization mechanisms to adapt various complicated environments. Researchers from biology, navigation and localization have respectively proposed a lot of neural localization models and biological inspired localization approaches, both of which can describe and mimic the neural localization mechanisms of animals to some extent. However, there are still some problems with these models, e.g., the precision of localization is low, the extensibility is weak, the navigation scale is small, the consistency with real neural mechanisms of animal is low, etc. To solve these problems, a novel Simultaneous Localization and Mapping (SLAM) approach which mimic the neural localization mechanisms of grid cells of animal’s brain is proposed in this project. The stereo-vision is provided as input to the proposed system, and a new path integration approach which can adapt to large scale environments and has better consistency with the real neural mechanisms of animal is developed through exploring and improving existing continuous attractor network models and oscillatory interference models of grid cells. A hippocampus place cell model and its relationships with grid cells and visual features are developed, and the accumulation errors associated with the proposed path integration system can be reduced by information fusion of visual features and path integration positions, so the accurate cognitive map can be built and represented. To improve the extensibility and accuracy of the model, a model parameters optimization process is proposed in the project. The project is expected to develop a novel biological inspired SLAM approach which can adapt to large scale environments, therefore a new technology for self-localization is presented; at the same time, the developed model is also expected to provide an effective validation tool for biological navigation and localization mechanisms in large scale environments.

英文关键词： Simultaneous Localization and Mapping;grid cell;place cell;continuous attractor network model;oscillatory interference model