基于自适应Kalman预测器的运动估计算法

利用图像序列估计目标运动速度是机器人视觉中的一项重要研究内容。它应用在机器人操作、导航、视觉跟踪等多项领域中。这些应用一般均要求运动估计算法具有较好的实时性和抗噪能力。卡尔曼滤波器和预测器正符合上述要求。该文基于运动图像的仿射模型，探讨从序列图像中预测目标三维平动速度的卡尔曼预测算法。首先建立运动目标的“当前”统计模型，然后根据运动图像的仿射模型找出图像运动参数与目标三维速度间的关系(图像运动参数由目标图像的几何矩计算获得)。最后结合自适应卡尔曼滤波和卡尔曼一步预测算法设计自适应卡尔曼一步预测器。为减轻预测器的发散性，对初始状态进行估计。仿真结果表明，基于“当前”统计模型和运动图像仿射模型设计出的自适应卡尔曼一步预测器具有较高的精度。

Adaptive Kalman Predictor-based Algorithms for Motion Estimation

Estimating object motion from image sequences is a problem in robot vision. Many applications of motion-from-images, including robot manipulation, navigation and visual tracking, require algorithms that can estimate motion on-line and have strong noiseproof feature. Kalman predictors can meet the above requirements. Based on the affine model of a moving image, this paper discusses a Kalman prediction algorithm for estimating an object's 3d translational velocity from image sequences. At first, the current statistical model of a moving object is established. Then, according to the affine model of a moving image, a mathematical relation between the 3-D translational velocity of a moving object and its image motion paremeters is found. Finally, an adaptive one-step Kalman predictor is designed. To alleviate divergence of the predictor, initial states is estimated. Simulation results show that the performance of the adaptive one-step Kalman predictor is good.