CAJ | 학술논문

针对标准快速同步定位与构图(FastSLAM)方法中由于样本退化及贫化导致自主水下航行器(Autonomous Underwater Vehicle, AUV)及路标位置估计精度严重下降的问题，该文提出一种基于迭代扩展 Kalman 滤波(Iterative Extended Kalman Filter, IEKF)建议分布和线性优化重采样的FastSLAM方法，通过IEKF融入最新观测值从而降低样本退化，为了降低样本的贫化，将重采样过程中复制的样本与部分被抛弃的样本通过线性组合产生新样本。建立 AUV 的运动学模型、特征模型及传感器的测量模型，通过 Hough 变换提取特征构建全局地图，采用改进的FastSLAM方法基于海试数据进行了AUV同步定位与构图试验，结果表明该文所设计的方法能够有效避免样本的退化及贫化，提高了AUV及路标的位置估计精度；此外，一致性分析结果表明所设计算法具有长期一致性。
침대표준쾌속동보정위여구도(FastSLAM)방법중유우양본퇴화급빈화도치자주수하항행기(Autonomous Underwater Vehicle, AUV)급로표위치고계정도엄중하강적문제，해문제출일충기우질대확전 Kalman 려파(Iterative Extended Kalman Filter, IEKF)건의분포화선성우화중채양적FastSLAM방법，통과IEKF융입최신관측치종이강저양본퇴화，위료강저양본적빈화，장중채양과정중복제적양본여부분피포기적양본통과선성조합산생신양본。건립 AUV 적운동학모형、특정모형급전감기적측량모형，통과 Hough 변환제취특정구건전국지도，채용개진적FastSLAM방법기우해시수거진행료AUV동보정위여구도시험，결과표명해문소설계적방법능구유효피면양본적퇴화급빈화，제고료AUV급로표적위치고계정도；차외，일치성분석결과표명소설계산법구유장기일치성。
The location estimated accuracy of Autonomous Underwater Vehicle (AUV) and landmarks decrease because of the degeneracy and impoverishment of samples in standard Fast Simultaneous Localization And Mapping (FastSLAM) algorithm. A improved FastSLAM algorithm based on Iterative Extended Kalman Filter (IEKF) proposal distribution and linear optimization resampling is presented in order to solve this issue. The latest observation is integrated with IEKF in order to decrease the sample degeneracy while the new samples are produced by the linear combination of copied samples and some abandoned ones in order to reduce the sample impoverishment. The kinematic model of AUV, feature model and the measurement models of sensors are all established. And then features are extracted with Hough transform to build the global map. The experiment of the improved FastSLAM algorithm with trial data shows that it can avoid the degeneracy and impoverishment of samples effectively and enhance the location estimation accuracy of AUV and landmarks. Moreover, the consistency analysis showed that the method possesses the consistency of long term.