基于改进扩展卡尔曼滤波算法的移动机器人定位方法研究
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摘要
针对移动机器人在位姿跟踪过程中存在单一传感器或多传感器测量系统对环境信息处理能力有限的问题,结合扩展卡尔曼滤波算法,对传感器测量信息进行融合分析.对于单个传感器测得的n个观测值,扩展观测矩阵至大于n的m个目标测量值,将预测空间到测量空间的映射设计为一个具有n个非零变量、维数为nm、秩为n的变换矩阵,实现传感器对状态向量的局部更新.在建立的传感器及机器人运动模型基础上,通过地面移动机器人进行实验验证.理论分析和实验结果表明,该方法能在保证定位精度的前提下,提高算法对不同传感器类型和传感器数量的泛化能力,增强测量系统的准确性和灵活性.
Aiming at the problem that single sensor or multi-sensor measurement system has limited ability to process environmental information in the process of position and orientation tracking of mobile robots,the author made the fusion analysis on the measurement information combine with the extended Kalman filter algorithm.For nobservations measured by a single sensor,the author extended the observation matrix to m target measurements and designed the mapping from the prediction space to the measurement space to a transformation matrix which has nnon-zero variables,nmdimension and rank nto achieve local updating of the state vector by the sensor.Experiments were taken on the ground mobile robot based on the established sensors and robot motion mathematical model.Theoretical analysis and experimental results show that the proposed method improves the generalization ability of the algorithm for different types and numbers of sensors under the premise of ensuring the positioning accuracy,meanwhile,enhance the accuracy and flexibility of the measurement system.
Aiming at the problem that single sensor or multi-sensor measurement system has limited ability to process environmental information in the process of position and orientation tracking of mobile robots,the author made the fusion analysis on the measurement information combine with the extended Kalman filter algorithm.For nobservations measured by a single sensor,the author extended the observation matrix to m target measurements and designed the mapping from the prediction space to the measurement space to a transformation matrix which has nnon-zero variables,nmdimension and rank nto achieve local updating of the state vector by the sensor.Experiments were taken on the ground mobile robot based on the established sensors and robot motion mathematical model.Theoretical analysis and experimental results show that the proposed method improves the generalization ability of the algorithm for different types and numbers of sensors under the premise of ensuring the positioning accuracy,meanwhile,enhance the accuracy and flexibility of the measurement system.
引文
[1]袁克非.组合导航系统多源信息融合关键技术研究[D].哈尔滨:哈尔滨工程大学,2012.
[2]王琪龙.基于异类传感器信息融合的目标跟踪理论与方法研究[D].北京:北京交通大学,2017.
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[5]米士彬,金振林.基于雅克比矩阵求解并联机器人位置正解方法[J].燕山大学学报,2011(5):391-395.Mi Shibin,Jin Zhenlin.Seeking positional forward solution of parallel mechanism based on Jacobin matrix[J].Journal of Yanshan University,2011(5):391-395.(in Chinese)
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[8]Moore T,Stouch D.A generalized extended Kalman filter implementation for the robot operating system[C]∥Springer,2014:335-348.
[9]王兴伟.基于ROS的临场移动机器人控制系统设计与研究[D].合肥:合肥工业大学,2017.
[10]张国伟.基于谷歌地图的智慧路灯管控系统设计与实现[D].合肥:合肥工业大学,2017.
[2]王琪龙.基于异类传感器信息融合的目标跟踪理论与方法研究[D].北京:北京交通大学,2017.
[3]何子述,夏威.现代数字信号处理及其应用[M].北京:清华大学出版社,2009.
[4]李兆洋.基于自适应卡尔曼滤波的GPS精密单点定位研究[D].成都:西南交通大学,2017.
[5]米士彬,金振林.基于雅克比矩阵求解并联机器人位置正解方法[J].燕山大学学报,2011(5):391-395.Mi Shibin,Jin Zhenlin.Seeking positional forward solution of parallel mechanism based on Jacobin matrix[J].Journal of Yanshan University,2011(5):391-395.(in Chinese)
[6]陈莹超.基于MEMS惯性传感器的行人三维自主定位系统研究[D].太原:中北大学,2017.
[7]包暑光,徐劭侠,常小林.GPS协同全站仪在印尼工程测量中的应用[J].科技创新与应用,2015(4):195.Bao Shuguang,Xu Xiaoxia,Chang Xiaolin.Application of GPS cooperative total station in engineering survey in indonesia[J].Technology Innovation and Application,2015(4):195.(in Chinese)
[8]Moore T,Stouch D.A generalized extended Kalman filter implementation for the robot operating system[C]∥Springer,2014:335-348.
[9]王兴伟.基于ROS的临场移动机器人控制系统设计与研究[D].合肥:合肥工业大学,2017.
[10]张国伟.基于谷歌地图的智慧路灯管控系统设计与实现[D].合肥:合肥工业大学,2017.