With the development of simultaneous positioning and mapping technology in the field of automatic driving, the current simultaneous localization and mapping scheme is no longer limited to a single sensor and is developing in the direction of multi-sensor fusion to enhance the robustness and accuracy. In this study, a localization and mapping scheme named LVI-fusion based on multi-sensor fusion of camera, lidar and IMU is proposed. Different sensors have different data acquisition frequencies. To solve the problem of time inconsistency in heterogeneous sensor data tight coupling, the time alignment module is used to align the time stamp between the lidar, camera and IMU. The image segmentation algorithm is used to segment the dynamic target of the image and extract the static key points. At the same time, the optical flow tracking based on the static key points are carried out and a robust feature point depth recovery model is proposed to realize the robust estimation of feature point depth. Finally, lidar constraint factor, IMU pre-integral constraint factor and visual constraint factor together construct the error equation that is processed with a sliding window-based optimization module. Experimental results show that the proposed algorithm has competitive accuracy and robustness.

Simultaneous Localization and Mapping (SLAM) has always been the focus of the robot navigation for many decades and becomes a research hotspot in recent years. Because a SLAM system based on vision sensor is vulnerable to environment illumination and texture, the problem of initial scale ambiguity still exists in a monocular SLAM system. The fusion of a monocular camera and an inertial measurement unit (IMU) can effectively solve the scale blur problem, improve the robustness of the system, and achieve higher positioning accuracy. Based on a monocular visual-inertial navigation system (VINS-mono), a state-of-the-art fusion performance of monocular vision and IMU, this paper designs a new initialization scheme that can calculate the acceleration bias as a variable during the initialization process so that it can be applied to low-cost IMU sensors. Besides, in order to obtain better initialization accuracy, visual matching positioning method based on feature point is used to assist the initialization process. After the initialization process, it switches to optical flow tracking visual positioning mode to reduce the calculation complexity. By using the proposed method, the advantages of feature point method and optical flow method can be fused. This paper, the first one to use both the feature point method and optical flow method, has better performance in the comprehensive performance of positioning accuracy and robustness under the low-cost sensors. Through experiments conducted with the EuRoc dataset and campus environment, the results show that the initial values obtained through the initialization process can be efficiently used for launching nonlinear visual-inertial state estimator and positioning accuracy of the improved VINS-mono has been improved by about 10% than VINS-mono.

Geomagnetic (magnetic)/pedestrian dead reckoning (PDR) integrated positioning, used in smartphone-based indoor positioning of pedestrians, is popular owing to its autonomous positioning and lack of a need for additional equipment. Single-point magnetic matching (SPMM) involves minimal computation and localization but is limited by its poor adaptability to diverse environments, few magnetic field features, and device heterogeneity. To solve these problems, we propose an improved SPMM/PDR based on the extended Kalman filter (EKF). First, we present a dynamic ${K}$ estimation for the weighted ${k}$ -nearest neighbor (WKNN) without previous information to compensate for the shortage of the WKNN with fixed ${K}$ when different signal distributions exist in different locations. Then, we propose a stochastic measurement model construction method with combined fingerprint accuracy indicators (CFAIs) to improve the adaptability to diverse environments. Finally, we fully exploit PDR’s high-accuracy relative localization to achieve adaptive compensation of the stochastic measurement model—adaptive EKF (AEKF)—to improve the accuracy and robustness of positioning. We compare three common methods based on five different smartphones, and the decrease in the positioning root mean square error (RMSE) values of the proposed AEKF-CFAI exceeds 16%, with the RMSE being lower than 0.9 m. Moreover, we investigate the influencing factors of the stochastic model construction and the impact of walking speed on AEKF-CFAI, finding that AEKF-CFAI is less affected by device heterogeneity and that AEKF has better robustness for different walking speeds than the CFAI-based method.

Emergent fields such as Internet of Things applications, driverless cars, and indoor mobile robots have brought about an increasing demand for simultaneous localization and mapping (SLAM) technology. In this study, we design a SLAM scheme called BVLI-SLAM based on binocular vision, 2D lidar, and an inertial measurement unit (IMU) sensor. The pose estimation provided by vision and the IMU can provide better initial values for the 2D lidar mapping algorithm and improve the mapping effect. Lidar can also assist vision to provide better plane and yaw angle constraints in weak texture areas and obtain higher precision 6-degree of freedom pose. BVLI-SLAM uses graph optimization to fuse the data of the IMU, binocular camera, and laser. The IMU pre-integration combines the visual reprojection error and the laser matching error to form an error equation, which is processed by a sliding window-based bundle adjustment optimization to calculate the pose in real time. Outdoor experiments based on KITTI datasets and indoor experiments based on the trolley mobile measurement platform show that BVLI-SLAM has different degrees of improvement in mapping effect, positioning accuracy, and robustness compared with VINS-Fusion and Cartographer, and can solve the problem of positioning and plane mapping in indoor complex scenes.

In complex urban environments, a single Global Navigation Satellite System (GNSS) is often not ideal for navigation due to a lack of sufficient visible satellites. Additionally, the heading angle error of a GNSS/micro-electro-mechanical system–grade inertial measurement unit (MIMU) tightly coupled integration based on the single antenna is large, and the attitude angle, velocity, and position calculated therein all have large errors. Considering the above problems, this paper designs a tightly coupled integration of GNSS/MIMU based on two GNSS antennas and proposes a singular value decomposition (SVD)-based robust adaptive cubature Kalman filter (SVD-RACKF) according to the model characteristics of the integration. In this integration, the high-accuracy heading angle of the carrier is obtained through two antennas, and the existing attitude angle information is used as the observation to constrain the filtering estimation. The proposed SVD-RACKF uses SVD to stabilize the numerical accuracy of the recursive filtering. Furthermore, the three-stage equivalent weight function and the adaptive adjustment factor are constructed to suppress the influence of the gross error and the abnormal state on the parameter estimation, respectively. A set of real measured data was employed for testing and analysis. The results show that dual antennas and dual systems can improve the positioning performance of the integrated system to a certain extent, and the proposed SVD-RACKF can accurately detect the gross errors of the observations and effectively suppress them. Compared with the cubature Kalman filter, the proposed filtering algorithm is more robust, with higher accuracy and reliability of parameter estimation.

The double-helical gear system was widely used in ship transmission. In order to study the influence of backlash on the nonlinear frequency response characteristics of marine double-helical gear system, according to the structural characteristics of double-helical gear transmission, considering the time-varying meshing stiffness, backlash, damping, comprehensive transmission error, external load excitation, and other factors, a three-dimensional bending-torsional-axial-pendular coupling nonlinear dynamic modeling and dynamic differential equation of 24-DOF double-helical gear transmission system were established. The Runge–Kutta numerical method was used to analyze the influence of backlash, time-varying meshing stiffness, damping, error and external load excitation on the amplitude frequency characteristics. The results show that the backlash can cause the runout of the double-helical gear system, and the system has first harmonic and second harmonic response. With the increase of backlash, the amplitude of the system increases and the jumping phenomenon remains unchanged. The amplitude frequency response of the system is stimulated by time-varying meshing stiffness and comprehensive transmission error, and restrained by damping and external load excitation. The vibration displacement amplitude of the system increases with the increase of vibration displacement and has little effect on the state change of the system. The vibration test of double-helical gear is carried out. The frequency response components obtained by numerical simulation are basically consistent with the experimental results, which proves the correctness of the theoretical calculation. It provides a technical basis for the study of vibration and noise reduction performance of double-helical gear.