三维传感器极大似然配准算法

基于测量噪声的影响,提出一种精确极大似然法(EML)。配准估计是通过求最大似然函数获得的。说明了EML准则包含两类部分可分变量(实际目标位置和传感器配准误差),提供了两步递归优化算法,并通过计算机仿真证明了极大似然算法用于多传感器配准处理的估计精度高,适用的范围广,解决了最小二乘配准算法中存在的局限性;当多个传感器相距较远时,该算法明显优于传统的最小二乘配准算法。

Removal of alignment errors in integrate system of 3-D sensor

An exact maximum likelihood(EML)registration algorithm is presented,which is based on the effects of measurement noise.The registration estimates are obtained by maximizing the likelihood function.It shows that the EML criteria contains two sets of partially separable variables(the actual target positions and the sensor registration errors)and a two-step recursive optimization algorithm is provided.Through a computer simulation,it proves that EML has high accuracy and extensive in the process of multipe sensor registration and the solution to the limitation in minimum double-multiply registration is given.The given algorithm outmatches traditional minimum double-multiply registration especially when sensors are far apart from each other.