Abstract: iming at the problem that nonlinear estimation results of Extended Kalman Filter-based Simultaneous Localization and Mapping(EKF-SLAM) algorithm are inconsistent,this paper proposes an Improved Extended Kalman Filter-based Simultaneous Localization and Mapping(IEKF-SLAM) algorithm with polynomial.And on this basis,it designs a tracking and registration algorithm of Augmented Reality(AR) on unknown scene including mapping and updating,tracking and registration two parallel modules.Mapping and updating module uses the IEKF-SLAM algorithm.The tracking and registration module after video frame is captured,camera pose is estimated by constructing a map library.Then video frames feature points are extracted and the feature points are matched to the map library.The pose of the camera is updated.Then virtual objects are rendered and registered.Experimental results show that the consistency of the IEKF-SLAM algorithm is superior to the EKF-SLAM algorithm,and the result of tracking and registration of AR is satisfactory.

Key words: Extended Kalman Filter(EKF), Simultaneous Localization and Mapping(SLAM), consistency, tracking and registration, Augmented Reality(AR)

摘要： 基于扩展卡尔曼滤波器的即时定位与地图构建(EKF-SLAM)算法存在非线性估计结果不一致的问题。为此，提出一种利用多项式改进扩展卡尔曼滤波器的SLAM算法IEKF-SLAM,在此基础上设计一种用于未知场景的增强现实跟踪注册算法,包含地图构建与更新、跟踪注册2个并 行模块。地图构建与更新模块利用IEKF-SLAM算法实现,跟踪注册模块在捕获视频帧后通过构建的地图库估计摄像机位姿,将提取视频帧中的特征点与地图库中的特征点进行匹配,并对摄像机位姿进行更新,实现虚拟物体的渲染注册。实验结果证明,IEKF-SLAM算法的估计结果 一致性优于EKF-SLAM算法,且增强现实的跟踪注册效果更好。

关键词: 扩展卡尔曼滤波器, 即时定位与地图构建, 一致性, 跟踪注册, 增强现实

CLC Number: 

• TP242