The field of automatic driving has always been a research hotspot. Based on inertial and odometer, dead reckoning is used to navigate for L4 and above level autonomous vehicles with less human intervention when GPS is blocked. The navigation accuracy is mainly determined by the alignment error and the drift accumulated over time. In this paper, we proposed a novel method to achieve initial alignment of inertial navigation for L4 and above level autonomous vehicles. The alignment is accomplished with a MEMS inertial measurement unit (MIMU) and single antenna global positioning system real-time kinematic (GPS-RTK). By matching the trajectory obtained from MIMU dead reckoning with the trajectory obtained from RTK, the initial heading angle is obtained, and the heading angle accuracy is improved through multiple iterations. In the case of RTK precision decreasing, the MIMU can still achieve lane-level navigation after the initial alignment. Because the alignment is accomplished in motion, there is no need for additional static alignment time before the vehicle starts. The field experiment results show that the method could achieve fast initial alignment in a few seconds in moving state with an alignment accuracy of 0.05.

The verticality of the borehole determines the firmness of the superstructure, so it is very important to measure the verticality of the borehole. However, the existing static manual measurement technology has low efficiency and poor accuracy. In order to realize dynamic measurement, reduce cost and improve measurement accuracy, this paper proposes the error mutual correction method for low-cost IMU according to the working characteristics of dynamic and static combination of drill pipe, and establishes an error online compensation model and verticality measurement model. The mutual compensation method of gyroscope and accelerometer errors suppresses the accumulation of measurement errors. In addition, the pile digger works in a complex environment, and a verticality measurement sensor that is waterproof, vibration-proof, high-temperature-proof, with online solution and wireless transmission is designed to improve the reliability of the device. Experiments show that the mutual correction verticality measurement method in this paper can achieve a measurement accuracy of 7% (3σ).

The transfer alignment algorithm is a key technology for the prelaunch attitude initialization of shipborne drones. The estimation results of the traditional filter-based transfer alignment algorithm depend on a priori information. Different levels of inertial navigation systems (INSs) necessitate the design of distinct filter parameters. Moreover, in complex working environments, improper filter parameter design often leads to suboptimal estimation results or even filter divergence. In light of the limitations of current methods, this article introduces an analytical transfer alignment algorithm that utilizes the time-varying characteristics of relative attitude. The method reveals the propagation mechanism of initial attitude error and relative installation error in the attitude update process, and a relative attitude nonlinear model of primary INS and secondary INS is developed. Equivalent rotation vectors and higher-order infinitesimal principles are used for model simplification and linearized approximation, and the decoupling of error sources is realized. Finally, through the two-axis platform sway and shipboard experiments, it is concluded that the proposed method verified can achieve the transfer alignment accuracy within 0.04° (3σ) for INS with gyro bias stability of 0.5°/h.