Due to the need for providing low-cost, small-size, and high-reliability calibration solution around the clock independently, the Micro-Electro-Mechanical System (MEMS) inertial measurement unit (IMU) is a hot topic currently. In this paper, we propose a new calibration method based on MEMS IMU with the assistance of the FOG IMU. Different from the traditional MEMS IMU calibration, a Four-Position-and-Three-Rotation (FPTR) calibration sequence is designed in the conditions of lacking turntable. The simulation result shows that all 24 calibration parameters converge through the calibration rotation scheme. An experiment result of qualitative analysis is presented, by comparing the traditional method and proposed calibration method, which reveals the repeatability and stability of the proposed method. In practical applications, this FPTR calibration method can effectively reduce the calibration time and improve the experimental efficiency. In summary, the proposed method is suitable for the mass production calibration of MEMS IMU, and possesses the ability to calibrate full parameters of MEMS IMU at once without turntable or other devices. The research outcomes can provide a practical basis for MEMS IMU plus FOG IMU calibration.

Autonomous localization methods for Unmanned Aerial Vehicles (UAVs) have significant application potential in complex environments. This paper presents a comprehensive survey of UAV localization techniques, focusing on both pure vision-based and sensor-assisted approaches. For pure vision-based localization, the survey emphasizes key technologies for feature descriptor generation, advancements in similarity measurement criteria, and optimized computational strategies. The impact of these technologies on improving computational efficiency and localization accuracy. In the context of sensor-assisted multi-source UAV localization, the applications of filtering-based fusion, optimization-based fusion, and deep learning-based fusion methods are discussed. A detailed analysis demonstrates the advantages of multi-modal data fusion in improving robustness and accuracy. Despite significant progress in localization accuracy and adaptability to complex environments, challenges remain in adapting to low-texture environments, optimizing fusion strategies, and addressing computational resource limitations. Finally, the paper discusses future directions for the research and implementation of UAV autonomous localization methods.

Aiming at the inability of low precision ordinary turntable to effectively measure the resolution of the high-precision fiber-optic gyroscope (FOG), this paper proposes a method for a high-precision FOG resolution test based on a low-performance turntable with the earth’s angular velocity as the input value. Taking a two-axis turntable with 1 arc-minute angular precision as an example, the Two-Degrees-Of-Freedom (2-DOF) theoretical angle range is obtained, through stationary gyro biaxial angular position error incremental output analysis. The method can be used for high-precision FOG including, 0.001 deg/h, 0.0001 deg/h, 0.00001 deg/h (three typical accuracies) resolution measurements. The detailed test steps are presented. Simulation results show that traditional test methods fail under the same working conditions, while the proposed method can effectively measure the high-resolution gyro resolution effective coverage within the theoretical angle range of the corresponding two-axis turntable. Experimental results demonstrate that the two-axis turntable based on with 1 arc-minute angular precision can effectively perform a high-precision FOG resolution of 0.005 deg/h.

The high noise and local deformation of multi-modal images reduce the accuracy of scale invariant feature transform (SIFT) image matching. To solve this problem, a new method based on the SIFT framework, which fuses the phase consistency optimization strategy and the gradient direction of principal component analysis (PCA) with the 8 direction of latitude reduction, is proposed in this paper. This method fuses the histogram of the orientated phase congruency (HOPC) method to extract the direction of the image, and adopts PCA to extract the main direction, which effectively solves the problem that the matching accuracy decreases due to the inversion of the direction of the image. Using the image phase instead of gradient intensity, the difficult problem of direction extraction is effectively solved when image edge characteristics are not obvious. Finally, the random sample consistency (RANSAC) algorithm is used to eliminate false match points. Simulation and experiments show that compared with the SIFT algorithm and PCA-SIFT algorithm, the proposed method improved the number of match points and matching accuracy, significantly reduced the mismatching rate. The statistical results show that the number of match points raised in this paper increases by 20.1% and 200% respectively compared with the former two algorithms.

Gait analysis mainly contains gait phase detection and step counting, providing effective standards for health status and setting appropriate limits for the amount of physical activity. A method based on magnetometer and inertial sensors data fusion is presented for gait phase detection and step counting conveniently and accurately. For gait phase detection, the transient state (turning points) can be detected using the amplitude of magnetic field generated by a permanent attached to the ankle. We can calculate accurate gait phase with gait event in a gait cycle, of which adaptive threshold is estimated based on turning points. For step counting, we can pick out preselected peaks by detecting the peak of the magnetic field intensity amplitude, processing inertial sensor data corresponding to continuous peaks of magnetic field intensity to realize adaptive step counting. The comparative experiment results show that for gait phase detection, average relative errors of foot-flat, push-off, swing, and foot-flat phase are 11.07%, 9.95%, 13.04%, and 10.92%, respectively, based on the proposed method to detect four phases; for step counting, the average accuracy of the presented method is 96.04% in walking without interference and 94.46% in walking with interference. Overall, our proposed method has an improvement in the anti-interference ability and accuracy.

In complex tracking scenarios of multi-target occlusion, especially when the target is seriously occluded, feature loss can lead to tracking failure. In this paper, we develop a multi-target occlusion tracking algorithm based on a 3D spatio-temporal context graph model, which can fully utilize the targets’ RGB-D information. Specifically, we first extract target’s 3D features containing appearance, motion, spatial and temporal features. Then, the spatio-temporal context features between different targets are also be acquired. Furthermore, the Attribute Relational Graph (ARG) model with target’s 3D spatio-temporal context features is constructed, which combines spatial features in the current frame and temporal features between consecutive frames. Moreover, if the target is determined to be occluded according to the matching results in the current frame, the probability relaxation method is used to assign the optimal tracking label to clustering blocks in occlusion area by matching these ARG models, which can address the multi-target cross occlusion problem. Extensive experiments on the Princeton Tracking Benchmark Datasets clearly demonstrate that our method can achieve superior performance against the typical algorithms proposed in recent years.