This paper studies the remaining useful life (RUL) of lubricating oil based on condition monitoring (CM). Firstly, the element composition and content of the lubricating oil in use were quantitatively analyzed by atomic emission spectrometry (AES). Considering the large variety of oil data obtained through AES, the accuracy and efficiency of the RUL prediction model may be reduced. To solve this problem, a comprehensive parameter selection method based on information entropy, correlation analysis, and lubricant deterioration analysis is proposed to screen oil data. Then, based on a support vector machine (SVM), the RUL prediction model of lubricant was established. By comparing the experimental results with the output data of the prediction model, it is shown that the accuracy and efficiency of the SVM prediction model established after parameter screening have been significantly improved.

Vehicle dynamics model-aided inertial navigation system (INS) is an effective approach for autonomous navigation when the Global Navigation Satellite System fails, However, the accuracy of the vehicle dynamics model (VDM) will decrease, or even diverge, in low-speed and high-dynamic situations, which leads to the performance degradation of the whole system. The traditional VDM-aided INS uses the velocity information from VDM to estimate and correct INS errors, but most previous research ignored that the acceleration and angular velocity from INS are more accurate than VDM. To reach the full potential of INS and VDM, this paper proposed a three-layer structure for the VDM-aided INS. In the proposed method, the navigation information is divided into the sensor layer, system layer I, and system layer II. In the sensor layer, VDM is corrected by INS through the strong tracking cubature Kalman filter, and the chi-square test is adopted to avoid redundant computation. In the system layer I, INS is aided by the corrected velocity information from VDM. In the system layer II, the position is updated with the velocity and attitude that are corrected in the system layer I. As the result, VDM and INS can be corrected in different layers simultaneously, and the accuracy and robustness of the whole system can be improved. The simulation experiment proved that, with the sensor layer filter, the velocity errors of VDM can be reduced by 35% in low-speed and high-dynamic situations, and reduced by 20% in the normal situation. Then, a 31-minute field test shows that the maximum horizontal position error of the proposed method is 241.8 m, while those of two traditional VDM-aided INS methods are more than 500 m.

Quickly grasping the surrounding environment’s information and the location of the vehicle is the key to achieving automatic driving. However, accurate and robust localization and mapping are still challenging for field vehicles and robots due to the characteristics of emptiness, terrain changeability, and Global Navigation Satellite System (GNSS)-denied in complex field environments. In this study, an LVI-SAM-based lidar, inertial, and visual fusion using simultaneous localization and mapping (SLAM) algorithm was proposed to solve the problem of localization and mapping for vehicles in such open, bumpy, and Global Positioning System (GPS)-denied field environments. In this method, a joint lidar front end of pose estimation and correction was designed using the Super4PCS, Iterative Closest Point (ICP), and Normal Distributions Transform (NDT) algorithms and their variants. The algorithm can balance localization accuracy and real-time performance by carrying out lower-frequency pose correction based on higher-frequency pose estimation. Experimental results from the complex field environment show that, compared with LVI-SAM, the proposed method can reduce the translational error of localization by about 4.7% and create a three-dimensional point cloud map of the environment in real time, realizing the high-precision and high-robustness localization and mapping of the vehicle in complex field environments.

The navigation accuracy is an important performance indicator for intelligent vehicles. Errors of the inertial navigation system (INS) diverge fast in the electromagnetic signal denial environment, and the vehicle model aid is a common approach to solve it. However, the accuracy of the vehicle model will be influenced by the road condition, vehicle motion state, and other interferences, so precision degradation and gross error are frequent. Against this problem, this article proposes the robust multilayer vehicle kinematics model (VKM)-aided INS. First, the gross error detection and isolation method is proposed based on the singular value decomposition (SVD) and chi-square test, and problems caused by the inadequate measurement redundancy are analyzed and solved by the “model redundancy.” Second, the multi-layer vehicle model-aided INS is proposed, which contains the sensor layer, the system layer I, and the system layer II. In the sensor layer, the soft and hard constraints of the vehicle model are defined, and the vehicle velocities are estimated with the constraints. Then, in the system layer I, the vehicle velocities from the sensor layer are used to estimate and correct INS errors. At last, the position is updated in the system layer II with the corrected attitude and velocity, so that the navigation performance can be improved. The simulation and field experiments proved that the proposed method has higher navigation accuracy and better robustness against gross error and high-dynamics situation and that its position error is less than 20 m in the 7-min field test.

An inner surface 3D reconstruction device structure is proposed in this paper to reconstruct the complex curved inner surface of a deep hole. First, the optical paths of the camera and the structured light transmitter are converged on the deep hole inner surface through plane mirrors to form a multi-line structured light projection system (MSLPS). The parameters of the MSLPS are then calibrated before the measurement. Second, the MSLPS rotates during the measurement process to obtain the point cloud collections of the inner surface and the rotation angles at different measurement positions are recorded. The point clouds contain the 3D surface information of the measured areas. Finally, the entire surface shape is reconstructed by the proposed reconstruction algorithm, and the feature parameters of the deep hole inner surface can be calculated by the reconstructed point cloud. The development process of the system was demonstrated in the experiment. The proposed system is validated through the reconstruction experiment of different areas in a deep hole.