论文标题
使用Kalman滤波器，机器学习和曲线拟合方法对3D重复式trajxtion进行准确预测和估计
Accurate Prediction and Estimation of 3D-Repetitive-Trajectories using Kalman Filter, Machine Learning and Curve-Fitting Method
论文作者
论文摘要
轨迹的准确估计和预测对于捕获任何高速目标至关重要。在本文中，使用扩展的卡尔曼滤波器（EKF）来跟踪轨迹的第一个循环中的目标，以收集数据点，然后将机器学习与最小二乘曲线拟合的组合组合用于准确估计后续循环的未来位置。 EKF从其视觉信息中估算目标的当前位置，然后通过使用观察顺序来预测其未来位置。我们利用来自三维轨迹的目标的嘈杂视觉信息来执行预测。所提出的算法是在Ros-Gazebo环境中开发的，并在硬件上实施。
Accurate estimation and prediction of trajectory is essential for the capture of any high speed target. In this paper, an extended Kalman filter (EKF) is used to track the target in the first loop of the trajectory to collect data points and then a combination of machine learning with least-square curve-fitting is used to accurately estimate future positions for the subsequent loops. The EKF estimates the current location of target from its visual information and then predicts its future position by using the observation sequence. We utilize noisy visual information of the target from the three dimensional trajectory to carry out the predictions. The proposed algorithm is developed in ROS-Gazebo environment and is implemented on hardware.
