基于动态和静态环境对象观测一致性约束的移动机器人多传感器标定优化方法

摘要

A calibration optimization method of camera and laser rangefinder based on stationary and moving object observation consistency constraint is proposed to address the problem of spatial observation consistency from heterogeneous multi-sensor in the process of mobile robot navigation in unknown environment.A covariance intersection method is used to fuse the bearing state of moving object on image plane,and Kalman and probabilistic data association filters are used to resolve the bearing state fusion of corner feature in the situations of "one-to-one" and "one-to-many".On this basis,the objective function is generated using the bearing errors before and after fusion of image projections of stationary and moving objects,and the calibration parameters of camera and laser rangefinder are optimized using nonlinear optimization method.Experimental results show that the proposed method can be used to improve the observation consistency of multi-sensor,and the effectiveness of the mentioned methods is verified.%为了解决机器人未知环境导航过程中的多源、异构传感器空间一致性观测问题,提出了基于动态和静态环境对象观测一致性约束的摄像机与激光测距传感器联合标定优化方法.利用协方差交集方法实现运动目标图像平面方向状态融合,同时采用卡尔曼滤波和概率数据关联滤波实现一对一和一对多信息源的静态角点特征图像平面方向状态融合;在此基础上,利用动态和静态物体融合前与融合后状态误差构造优化目标函数,并利用非线性优化方法实现标定参数优化.实验结果表明,该设计方法能够提高多传感器环境观测的一致性水平,验证了该方法的有效性.