论文部分内容阅读
针对里程计在定位过程中存在累积误差的问题,建立了一种通用的移动机器人里程计误差模型,对里程计误差进行实时反馈补偿.在利用激光雷达进行环境特征提取过程中,根据激光雷达原始数据存在的误差,建立了激光雷达的观测误差模型,并根据环境特征和机器人的相对位置关系,建立了移动机器人观测模型.最后,结合里程计和激光雷达误差模型,利用扩展卡尔曼滤波(EKF)实现了基于环境特征跟踪的移动机器人定位.实验结果验证了里程计和激光雷达误差模型的引入,在增加较短定位时间的情况下,可以有效地提高移动机器人的定位精度.
Aimed at the accumulated error of odometer in positioning process, a universal error model of mobile robot odometer is established to compensate real-time error of odometer error.In the process of extracting feature of environment by using lidar, According to the error of the data and the error of the data, the observational error model of lidar is established, and the observational model of mobile robot is established according to the relationship between the environment characteristic and the relative position of the robot.Finally, combining with the error model of odometer and lidar, ) Has realized the localization of mobile robot based on environment feature tracking.The experimental results verify the introduction of odometer and lidar error model, which can effectively improve the positioning accuracy of mobile robot under the condition of increasing the short positioning time.