The traditional Kalman filtering-based transfer alignment methods largely depend on prior information for initialisation. However, the initialisation process is hard to fulfil on a moving base. In this paper, a type of inertial measurement vector integration matching for optimisation-based transfer alignment and calibration is proposed to estimate the misalignment between the Master Inertial Navigation System (MINS) and Slave Inertial Navigation System (SINS), and main inertial sensor error parameters of SINS, including bias and scale factor error. In contrast to filter techniques, the proposed method has the capability of self-initialisation and provides a new idea to solve the alignment and calibration problem. No prior information is needed. Moreover, the integration time interval for the matching inertial measurement vector is selected by considering both the observation degree of inertial sensor error parameters and the noise effect. Simulation results demonstrate that the proposed method has faster convergence and is more accurate than Kalman filter techniques.

This paper presents a novel systematic approach to identify the dynamic parameters of robotic manipulators. A sequential identification procedure is first proposed to deal with the difficulties usually encountered with standard approaches. An all-accelerometer inertial measurement unit (IMU) is also suggested to estimate the joint velocities and accelerations, which are traditionally obtained by differentiating the joint positions. The IMU kinematics and the computation method for estimation joint motion from IMUs are provided. The proposed method yields promising results in improving the identification precision compared to conventional methods. Finally, practical experiments are conducted to validate the theoretical results.