无人机Unscented FastSLAM算法研究

摘要

同时定位与作图（SLAM）成为使运动载体真正自主导航的必要前提，因此成为近来研究的热点问题。FastSLAM作为一种成功的SLAM研究方法吸引了许多学者的关注。FastSLAM把SLAM问题分解为一个定位问题和一个作图问题，地图由EKF滤波进行估计。提出了一种用于无人机（UAV）的改进的FastSLAM算法，使用UKF来代替EKF估计地标的位置，可以改善估计的精度，同时，避免线性化传感器的观测模型及计算雅可比矩阵。%Simultaneous localization and mapping(SLAM)is a necessary prerequisite to make mobile vehicle truly autonomous,which is a hot research topic today. FastSLAM as a successful SLAM method abstracts many researchers’attentions. FastSLAM factors the SLAM problem into a localization problem and a mapping problem in which the landmark position is estimated by EKF. A modified FastSLAM is presented for uninhabited aerial vehicle(UAV),using UKF to replace the EKF to estimate the landmark position. So we can improve the estimation precision,at the same time no need to linearize the sensor observation model and to compute its Jacobian matrix.