【作者】 柴红霞；

【导师】 冯林；

【作者基本信息】 大连理工大学 ， 计算机应用技术， 2010， 硕士

【摘要】 同步定位与地图创建(SLAM)问题是移动机器人解决在未知环境中实现自主的关键问题。然而,在未知的环境中,移动机器人系统缺乏先验的地图信息和定位信息,它只能通过对环境特征的感知与估计来进行定位,同时利用定位信息来进行地图创建。数据关联的问题是SLAM研究重点,本文的工作重点就是对SLAM中数据关联技术的相关方法进行研究与探讨。本文根据数据关联树模型和贝叶斯图模型,提出了一种解决同时定位与地图创建中数据关联问题算法,即基于动态阈值的启发式搜索SLAM算法(DHBS_SLAM);该方法通过对数据关联树有限深度的回溯实现对错误数据的修正,在搜索的过程中使用动态阈值进行门限过滤,减少可能的数据关联的数目,在不降低数据关联正确率的情况下,提高数据关联效率,最终实现算法的在线修正过去错误数据关联。本文对机器人相关的模型进行了研究,结合了SLAM的问题中的一般模型创建了简化的仿真平台。借助这个平台,对FastSLAM的方法,HBS_SLAM方法和DHBS_SLAM方法进行了实验,并对实验结果从关联正确率和关联时间两方面上进行了比较、分析。实验结果表明DHBS_SLAM算法是一种有效的机器人同时定位与地图创建方法。更多还原

【Abstract】 The simultaneous localization and mapping (SLAM) problem is one of the key problems for a mobile robot to be truly autonomous in an unknown environment. However, in an unknown environment, for lack of priori map information and location information the mobile robot locates by the perception of environmental characteristics and estimate and at the same time to create map. Data association is a important part of SLAM. In this paper, we research and discuss the data association in SLAM.In this paper, by analyzing data association tree model and Bayesian model, there is a method to resolve data association problem for SLAM which is called as an SLAM algorithm based on heuristic graph search with dynamic threshold. This method uses back-searching to revise past error data associations and uses dynamic threshold to reduce possible associations. The algorithm doesn’t lower the accuracy of data association and doesn’t take more time to revise the past error data associations on line.The paper has done a lot of researches on robot’s model. At the end, A simulation platform is created using the models to do experiments. Using the platform, we do experiments using FastSLAM, HBS_SLAM and DHBS_SLAM to test algorithm’s performance. And do some analysises on the experiment’s results. The accuracy of data association and time of data association improve that the DHBS_SLAM is a efficient algorithm for SLAM.更多还原