Online asymmetry estimation for whole angle mode coriolis vibratory gyroscopes by high frequency injection

The whole angle mode gyroscope(WAMG)is considered to be the next generation architecture,but it is suffered from the asymmetry errors to conduct real products.This paper proposes a novel high frequency injection based approach for the error parameters online identification for the WAMG.The significance is that it can separate physical and error fingerprints to enable online calibration.The nonlinear WAMG dynamics are discretized to meet the requirement of numerical precision and computation efficiency.The optimized estimation methods are then constructed and compared to track asymmetry error parameters continuously.In the validation part,its results firstly prove that the proposed scheme can accurately identify constant asymmetry parameters with an overall tracking error of less than 1 ppm and the extreme numerical convergence can reach 10^(-12)ppm.Under the dynamic asymmetry variation condition,the root mean square errors(RMSE)indicate that the tracking accuracy can reach the level of10^(-3),which shows the robustness of the proposed scheme.In summary,the proposed method can effectively estimate the WAMG asymmetry errors online with satisfied performance and practical values.

Development of a Calibration and Monitoring System for GD-1 High Pressure Common Rail Diesel Engine

Based on CAN calibration protocol, a new calibration and monitoring system was developed for the GD-1 high pressure common rail diesel engine. CAN driver block, monitoring program and calibration program for this system were designed respectively. The inquiry mode was used in the monitoring program and the interrupt mode was used in calibration program. The calibration program was designed in structural programming model. This system provides a reliable, accurate and quick CAN bus between ECU and PC, with baud rate up to 500Kbit/s. The implementation of the compatible and universal CAN calibration protocol makes it easy to displace the system and its function modules. It also provides friendly, compatible and flexible calibration interface, and the functions of online calibration and real-time monitoring. This system was successfully used in a GD-1 high pressure common rail diesel engine and the engine performance and exhaust emissions were significantly improved.

Lidar-Visual-Inertial Odometry with Online Extrinsic Calibration

To achieve precise localization,autonomous vehicles usually rely on a multi-sensor perception system surrounding the mobile platform.Calibration is a time-consuming process,and mechanical distortion will cause extrinsic calibration errors.Therefore,we propose a lidar-visual-inertial odometry,which is combined with an adapted sliding window mechanism and allows for online nonlinear optimization and extrinsic calibration.In the adapted sliding window mechanism,spatial-temporal alignment is performed to manage measurements arriving at different frequencies.In nonlinear optimization with online calibration,visual features,cloud features,and inertial measurement unit(IMU)measurements are used to estimate the ego-motion and perform extrinsic calibration.Extensive experiments were carried out on both public datasets and real-world scenarios.Results indicate that the proposed system outperforms state-of-the-art open-source methods when facing challenging sensor-degenerating conditions.

Improving the underwater navigation performance of an IMU with acoustic long baseline calibration

Underwater acoustic Long-Baseline System(LBL)is an important technique for submarine positioning and navigation.However,the high cost of the seafloor equipment and complex construction of a seafloor network restrict the distribution of the LBL within a small area,making an underwater vehicle difficult for long-distance and high-precision acoustic-based or inertial-based navigation.We therefore propose an acoustic LBL-based Inertial Measurement Unit(IMU)calibration algorithm.When the underwater vehicle can receive the acoustic signal from a seafloor beacon,the IMU is precisely calibrated to reduce the cumulative error of Strapdown Inertial Navigation System(SINS).In this way,the IMU is expected to maintain a certain degree of accuracy by relying solely on SINS when the vehicle reaches out the range of the LBL network and cannot receive the acoustic signal.We present the acoustic LBL-based IMU online calibration model and analyze the factors that affect the accuracy of IMU calibration.The results fulfill the expectation that the gyroscope bias and accelerometer bias are the main error sources that affect the divergence of SINS position errors,and the track line of the underwater vehicle directly affects the accuracy of the calibration results.In addition,we deduce that an optimal calibration trajectory needs to consider the effects of the three-dimensional observability and position dilution of precision.In the experiment,we compare the effects of seven calibration trajectories:straight and diamond-shaped with and without the change of depth,and three sets of curves with the change of depth:circular,S-shaped,and figure-eight.Among them,we find that the figure-eight is the optimal trajectory for acoustic LBL-based IMU online calibration.We take the maintenance period during which the accumulated SINS Three Dimensional(3D)position errors are below 1 km to evaluate the calibration performance.The filed experimental results show that for the Micro-electromechanical Systems-grade IMU sensor,the maintenance period for the IMU calibrated with the proposed algorithm can be increased by 121%and 38.9%compared to the IMU without calibration and with the laboratory default parameter calibration,indicating the effectiveness of the proposed calibration algorithm.

Optimal design and experiment research of an orthogonal-parallel six-axis force/torque sensor

A novel orthogonal-parallel six-axis force/torque sensor is studied based on a modified Stewart platform architecture,and the optimal design and experiment research of the sensor are discussed.Firstly,the model of orthogonal parallel six-axis force/torque sensor based on improved Stewart platform architecture and its static mathematical model are proposed.Secondly,according to the actual working condition of the sensor,the sensor is optimized and the optimal solution is obtained.Then,the experimental prototype and calibration system is developed.Finally,the superiority of the sensor structure and the effectiveness of the optimization method are verified by calibration experiments.The results of the proposed method are useful for the further research and application of the orthogonal-parallel six-axis force/torque sensor.

Lightweight hybrid visual-inertial odometry with closed-form zero velocity update

Visual-Inertial Odometry(VIO) fuses measurements from camera and Inertial Measurement Unit(IMU) to achieve accumulative performance that is better than using individual sensors.Hybrid VIO is an extended Kalman filter-based solution which augments features with long tracking length into the state vector of Multi-State Constraint Kalman Filter(MSCKF). In this paper, a novel hybrid VIO is proposed, which focuses on utilizing low-cost sensors while also considering both the computational efficiency and positioning precision. The proposed algorithm introduces several novel contributions. Firstly, by deducing an analytical error transition equation, onedimensional inverse depth parametrization is utilized to parametrize the augmented feature state.This modification is shown to significantly improve the computational efficiency and numerical robustness, as a result achieving higher precision. Secondly, for better handling of the static scene,a novel closed-form Zero velocity UPda Te(ZUPT) method is proposed. ZUPT is modeled as a measurement update for the filter rather than forbidding propagation roughly, which has the advantage of correcting the overall state through correlation in the filter covariance matrix. Furthermore, online spatial and temporal calibration is also incorporated. Experiments are conducted on both public dataset and real data. The results demonstrate the effectiveness of the proposed solution by showing that its performance is better than the baseline and the state-of-the-art algorithms in terms of both efficiency and precision. A related software is open-sourced to benefit the community.