无人工标记室内环境下SLAM技术的研究

发布时间：2018-03-09 02:05

本文选题：实时定位与地图创建　切入点：移动机器人　出处：《山东大学》2017年硕士论文　论文类型：学位论文

【摘要】：SLAM(Simultaneous Localization And Mapping)技术,我们平时称之为实时定位与地图创建,顾名思义,指的是机器人处在一个未知的环境中,从毫无所知,到完全熟悉该环境,并在机器人的"记忆中"创建出一张完整的环境地图,根据这张地图可以完成自定位与导航任务,这个过程需要用到机器人内部自带的一些传感器,同时也需要外界的部分信息。本课题以搭载了 ROS的移动式服务机器人为平台,进行了机器人SLAM研究,进行了机器人运动模型搭建、数据信息的获取和处理,完成了室内地图的创建,利用所创建的地图进行了机器人运动的导航,最后进行了实验和仿真,证明了方法的可行性。本文从以下几个方面进行了研究:(1)以自主研发的移动机器人为平台,介绍了该款机器人系统的组成,包括系统参数配置,硬件和软件模块,同时建立了运动学模型,便于今后对机器人运动的控制。(2)使用Kinect和SickTIM561进行外部信息的采集和处理,使用IMU和编码器进行机器人内部信息的采集,同时使用外部信息和内部信息进行多传感器信息融合,利用融合之后的信息进行定位与导航,可以提高整个系统的稳定性,同时也减少了因为某一方面的信息原因出现的偏差。(3)采用相对简单的线条表示地图的方法提高了地图的更新速度,克服了地图信息表示不全的缺点。建立了机器人状态模型并将扩展卡尔曼滤波方法用于本系统中,实时更新和预测机器人状态,包括机器人位置、速度、观测信息等。(4)在ROS的RVIZ和GAZEBO中进行仿真实验,对多个环境进行了探索,成功地创建环境地图并利用所创建的地图进行导航与定位,给定机器人一个目标位置,机器人会根据所创建的地图自主进行避障、路径规划和导航,同时在这期间我们可以实时观察到机器人的一系列信息,包括运动速度、全局路径规划、局部路径规划等。(5)进行了机器人SLAM中相关数据的分析,对实验中的一些问题作了说明,总结了本课题的成果,提出了下一步将要进行的任务。
[Abstract]:SLAM(Simultaneous Localization And mapping, which we call real-time location and map creation, as the name implies, refers to a robot in an unknown environment, from being unknown to being fully familiar with the environment. And create a complete environmental map in the "memory" of the robot, according to which the task of self-localization and navigation can be accomplished, and this process requires some sensors brought in by the robot. At the same time, it also needs some information from outside. Based on the mobile service robot with ROS as the platform, the research of robot SLAM is carried out, and the robot motion model is built, and the data information is acquired and processed. The building of indoor map is completed, and the navigation of robot motion is carried out by using the created map. Finally, the experiment and simulation are carried out. The feasibility of the method has been proved. In this paper, the following aspects of the research on the mobile robot developed by ourselves as a platform, introduced the composition of the robot system, including system parameters configuration, hardware and software modules, At the same time, the kinematics model is established to facilitate the control of robot motion in the future. The Kinect and SickTIM561 are used to collect and process the external information, and the IMU and encoder are used to collect the internal information of the robot. At the same time, using external and internal information for multi-sensor information fusion, using the fused information for positioning and navigation, can improve the stability of the whole system. At the same time, it also reduces the deviation due to a certain aspect of information.) the method of using relatively simple lines to represent the map improves the speed of updating the map. The state model of the robot is established and the extended Kalman filter method is used to update and predict the state of the robot in real time, including the position and speed of the robot. The observation information is simulated in the RVIZ and GAZEBO of ROS, and several environments are explored. The environment map is created successfully, and the map is used to navigate and locate, and the robot is given a target position. The robot will independently perform obstacle avoidance, path planning and navigation based on the maps created, and during this time we can observe a range of information about the robot in real time, including speed of movement, global path planning, In this paper, we analyze the related data in the robot SLAM, explain some problems in the experiment, summarize the results of this project, and put forward the tasks to be carried out in the next step.
【学位授予单位】：山东大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP242

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