Abstract: Aiming at autonomous positioning of wheeled warehous robots, an indoor positioning method based on visual landmark and odometer data fusion was proposed. Firstly, by establishing a camera model, the rotation and translation relationship between the beacon and the camera was cleverly solved to obtain the positioning information. Then, based on the analysis of the characteristics of the angle difference between the gyroscope and the odometer, a method of angle fusion based on variance weight was proposed to deal with low update frequency and discontinuous positioning information problems. Finally, to compensate for a single sensor positioning defect, the odometer error model was designed to use a Kalman filter to integrate odometer and visual positioning information. The experiment was carried out on differential wheeled mobile robot. The results show that by using the proposed method the angle error and positioning error can be reduced obviously, and the positioning accuracy can be improved effectively. The repeat positioning error is less than 4 cm and the angle error is less than 2 degrees. This method is easy to operate and has strong practicability.

Key words: indoor positioning, multi-sensor information fusion, Kalman Filter (KF), mobile robot, landmark

摘要： 针对轮式仓储物流机器人的自主定位问题，提出了一种基于视觉信标和里程计数据融合的室内定位方法。首先，通过建立相机模型巧妙地解算信标与相机之间的旋转和平移关系，获取定位信息；然后，针对信标定位方式更新频率低、定位信息不连续等问题，在分析陀螺仪和里程计角度误差特点的基础上，提出一种基于方差加权角度融合的方法实现角度融合；最后，设计里程计误差模型，使用Kalman滤波器融合里程计和视觉定位信息弥补单个传感器定位缺陷。在差分轮式移动机器人上实现算法并进行实验，实验结果表明上述方法在提高位姿更新率的同时降低了角度误差和位置误差，有效地提高了定位精度，其重复位置误差小于4 cm，航向角误差小于2°。同时该方法实现简单，具有很强的可操作性和实用价值。

关键词: 室内定位, 多传感器信息融合, Kalman滤波(KF), 移动机器人, 人工路标