Redundant manipulators are suitable for working in narrow and complex environments due to their flexibility. However, a large number of joints and long slender links make it hard to obtain the accurate end-effector pose of the redundant manipulator directly through the encoders. In this paper, a pose estimation method is proposed with the fusion of vision sensors, inertial sensors, and encoders. Firstly, according to the complementary characteristics of each measurement unit in the sensors, the original data is corrected and enhanced. Furthermore, an improved Kalman filter (KF) algorithm is adopted for data fusion by establishing the nonlinear motion prediction of the end-effector and the synchronization update model of the multirate sensors. Finally, the radial basis function (RBF) neural network is used to adaptively adjust the fusion parameters. It is verified in experiments that the proposed method achieves better performances on estimation error and update frequency than the original extended Kalman filter (EKF) and unscented Kalman filter (UKF) algorithm, especially in complex environments.

中文翻译：

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使用互补增强和自适应融合的复杂环境中的末端执行器姿势估计

冗余机械手具有灵活性，因此适合在狭窄和复杂的环境中工作。但是，大量的关节和细长的链接使直接通过编码器获得冗余机械手的精确末端执行器姿势变得困难。本文提出了一种结合视觉传感器，惯性传感器和编码器的姿态估计方法。首先，根据传感器中每个测量单元的互补特性，对原始数据进行校正和增强。此外，通过建立末端执行器的非线性运动预测和多速率传感器的同步更新模型，采用改进的卡尔曼滤波器（KF）算法进行数据融合。最后，径向基函数（RBF）神经网络用于自适应地调整融合参数。