基于UKF的室内移动机器人定位问题

定位是移动机器人必须具备的基本要求,在室内环境中,物体大多是多边形,因此可由线段进行描述,由激光传感器获取环境信息,采用哈夫变换对激光数据进行分簇,最小二乘法拟合线段特征,并建立里程计模型和观测模型;在传统的EKF定位算法中,线性化处理会导致计算精度下降,甚至造成滤波器的不稳定,且推导Jacobian矩阵比较困难;为了克服EKF的缺点,文章采用UKF算法将里程计数据和激光传感器数据进行融合,无需进行线性化处理,可获得高精度的移动机器人的位姿;通过实验,验证了基于UKF的定位算法在定位的精确性上优于传统的EKF定位算法。

SLAM of indoor mobile robots based on the Unscented Kalman Filter

Localization is a fundamental requirement for a mobile robot.In indoor environments,the objects are polygonal usually.These objects can be described as line segments.The laser sensor can obtain the information of the environment automatically.By making use of the Hough transform technology,laser sensor data can be divided into different clusters.Then the least-square method is used to extract features of line segments.The odometry model and the observation model are established.In the Extended Kalman Filter(EKF) algorithm,a nonlinear system is linearized to a linear system,which may lead to the decline of accuracy and even instability of the filter,and it is hard to calculate Jacobian matrices.In order to overcome the disvantages of the EKF,the Unscented Kalman Filer(UKF) is used to integrate the data from odometry and the laser sensor.By using this method,the accurate pose of the mobile robot can be obtained without linearizing the system model.It is proved by experiments that the algorithm based on the UKF is obviously more accurate than the algorithm based on the EKF.