室内环境下基于拟大脑皮层模型的主动式视觉SLAM研究

项目名称： 室内环境下基于拟大脑皮层模型的主动式视觉SLAM研究

项目编号： No.61203338

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 自动化学科

项目作者： 张新征

作者单位： 暨南大学

项目金额： 25万元

中文摘要： 针对现有主动SLAM的自主性与智能水平存在一些局限性的问题,本项目以提高主动SLAM的智能性为目标,根据描述大脑皮层工作原理的记忆?预测理论及相关的拟大脑皮层模型,提出一种新颖的主动式视觉SLAM系统。该系统的研究以拟大脑皮层模型为基础, 首先建立环境的外观图像序列,然后将模型的学习功能与外观制图方法相结合实现地图创建;探索拟大脑皮层模型的推理功能与数据关联问题的关系,根据贝叶斯理论与机器学习的方法提出相关算法。其次引入行为功能模块对拟大脑皮层模型加以改进,在此基础上利用贝叶斯规划方法与动态场理论,探究基于拟大脑皮层模型预测功能的感知-运动协调机理,设计与其相关的动作选择和定位算法。最后整合制图、定位与动作选择各环节构建完整SLAM系统,进一步研究无先验信息时的动作选择方法与SLAM系统性能评估策略。本项目以期拓展和丰富主动SLAM研究,在理论和技术上为解决该问题提供一种新思路与新方法。

中文关键词： 同时制图与定位；层级实时记忆模型；感知—运动整合；外观制图；大脑皮层学习算法

英文摘要： To address the prolem that current active-mode SLAM systems have limitations on autonomy and intelligence, a novel active-mode visual SLAM framework is proposed in this project. The study of this new framework is based on the memeroy-prediction theory, which presents the basic principle of the neocortex, and its related simulated neocortex model (SNM). Firstly, after the appearance image sequceses of environments are constructed, map building process is designed and implemented by incorporating learning ability of SNM and appearance based mapping technology. The relationship between the data association problem and the inference capability of SNM is explored, and the data association algorithm is also proposed using Bayesian theory and machine learning method. Secondly, a behavior module is developed and inserted into the SNM for improvement. With this improved SNM, on the basis of the prediction ability of SNM the mechanism of sensory-motor coordination is investigated by applyng Baysian programming and dynamic field theory, and furthermore action selection and localiztion algorithms related to the sensory-motor coordination are developed. Finally, mapping, localization and action selection are integrated to build the whole active-mode visual SLAM, and without any priori information the method of action selecti

英文关键词： SLAM；hierarchical temporal memroy (HTM) model；sensory-motor integration；appearance based mapping；cortex learning algorithm