实时运动结构重建在自主导航系统中的应用

实时运动结构重建是自主车辆、机器人导航、空间探测器自主降落、智能监控等领域中的重要研究课题。目前实时运动结构重建主要存在着特征匹配困难、鲁棒性差、系统无法自动获取初始参数和需要大量人工干预等诸多问题。利用高速CMOS摄像机与惯性传感数据融合提高了运动结构重建算法的精度及其鲁棒性。该算法在扩展卡尔曼滤波框架下是通过融合惯性与视觉传感器的数据来进行运动估计的。对场景中的每一个待估计结构的特征点建立对应的卡尔曼滤波器,以估计其空间三维结构信息。运动估计模块与结构估计模块交替运行,减小了系统运算的复杂度,提高了实时性能。通过对真实场景图像序列的实验验证结果表明,惯性传感器的额外信息能够有效地提高运动结构估计的精度,能够增强算法的鲁棒性。

Structure and motion by sensor fusion for autonomous navigation system

Real-time structure and motion is the most important research direction,which can be applied in vehicle navigation,spacecraft landing,intelligent monitoring system.Existing vision based structure and motion algorithms are too fragile and tend to drift.How the fusion of inertial and vision data can be used to gain robustness is investigated.The fusion is based on Kalman filtering,using an Extended Kalman filter to fuse inertial and vision data,and a bank of Kalman filters to estimate the sparse 3D structure of the real scene.Two frame feature-based motion estimation is used for initial pose estimation.The motion and structure estimation filters work alternately to recover the sensor motion,scene structure and other parameters.The performance of this algorithm has been tested on real image sequences.Experimental results show that additional inertial data not only can be used to improve position accuracy of reconstructed features and motion estimation,but also can enhance the robustness of the algorithm.