【作者】 李波；

【导师】 施家栋；

【作者基本信息】 北京理工大学 ， 兵器工程， 2015， 硕士

【摘要】 本文主要研究移动机器人导航中的同步定位与地图创建技术,移动机器人在自主导航过程中进行自身定位的同时不断通过传感器获取环境信息以建立环境地图。当移动机器人在行进过程中出现故障时,可以利用之前定位信息和环境地图信息返回到起始位置,能够提高移动机器人在执行任务中的生存能力,具有很高的研究价值。本文以履带式移动机器人为研究对象,建立机器人的运动学模型,基于该模型推导出航迹推算定位法的实现过程,并通过仿真和实验对航迹推算法的优缺点进行分析。创建基于激光雷达的特征地图,先借助激光雷达获取环境信息,从中提取出直线特征,再将当前特征和已经创建的地图进行匹配来更新地图。针对同步定位与地图创建问题,采用扩展卡尔曼滤波来对机器人位置和地图特征位置进行联合估计,完成了基于扩展卡尔曼滤波的同步定位与地图创建的预测与更新过程,并利用仿真实验验证该算法的有效性。更多还原

【Abstract】 This paper mainly studies the technology of simultaneous localization and mapping in the field of mobile robot navigation. With this technology t he mobile robot can know its own location and simultaneously build environment map by environment information getting from sensor. The mobile robot can come back to the starting position taking advant age of location information and the surrounding environment before when the communication is interrupted, which improves the level of mobile robots to viability in the process of performing tasks.Obviously,the technology has important research value.Taking tracked mobile robot as research object,a model of kinematic model of the robot is established. Deriving the implementation process of dead reckoning location based on the model, and analyzing the advantages and disadvantages of dead reckoning through simulation and experiments.Building a feature map based on laser radar, firstly gets envi ronment information using laser radar, extracts line features from this information, then matches the current features and map which has been created to update the map.Adopting extended Kalman filter to joint estimate the robot location and map feature location in order to solve simultaneous localization and mapping problem, accomplishing prediction and updated process of the simultaneous localization and mapping algorithm based on extended Kalman filter.Finally,the simulation results show this algorithm is effective.更多还原