FDO-Calibr: visual-aided IMU calibration based on frequency-domain optimization

In a deeply-coupled GPS/INS integrated system, the use of the inertial aiding information can improve the tracking loop performance and make the system more robust. To meet this requirement, the inertial aiding information should have sufficient accuracy in short-term (such as the sampling interval of GPS, e.g. 1sec). The MEMS (Micro-Electro Mechanical System) IMU (Inertial Measurement Unit) can be a promising candidate due to its small size and low cost. There should be no doubt that MEMS INS (Inertial Navigation System) can aid the GPS receiver tracking loop by eliminating the dominant part of the motion dynamic stress, considering that the INS errors induced by the receiver motion dynamics is much less than the motion dynamic itself, when the receiver manoeuvres. So the only concern the side effect caused by MEMS INS, which determine whether MEMS IMU is qualified for deep integration, is its navigation error independent with the motion dynamics (i.e. manoeuvre-independent error). This paper assesses this side effect of MEMS INS in terms of providing Doppler aiding data in to the GPS carrier tracking loop through a thorough error propagation analysis. The Laplace transform analysis is applied to the simplified INS error dynamic equations under stationary condition and find out the transfer relation between the error sources and the velocity estimation errors. Then the velocity error is converted to Doppler aiding error and substitute into the GPS tracking loop to analyze the corresponding carrier phase error. Results show that the largest velocity error caused by maneuver-independent errors is less than 0.1m/s during the typical GPS update interval (e.g. 1 sec), which meets the real road test results. The consequent carrier phase tracking error caused by the maneuver-independent error of MEMS INS is below 1.2 degree, which is much less than receiver inherent errors (e.g. the oscillator error and thermal noise). Conclusion can be reached that even the low-end MEMS IMUs have the ability of aiding the GPS receiver signal tracking although it induces some additional errors.

In this study, we built a monocular visual inertial odometry (VIO) dataset with a proposed self-calibrating platform for an inertial measurement unit (IMU). For a learning-based method, lack of database is the biggest limitation. One goal of this study is to generate a high-quality VIO database. Meanwhile, a properly calibrated inertial measurement unit (IMU) is critical for improving VIO system accuracy and precision. However, a professional IMU calibration tool is usually expensive and large. The proposed self-calibrating platform shows its benefits in eliminating the manual work in calibration and reducing the cost of the expensive platform, which brings more opportunities to students and researchers. This self-calibrating system was useful for collecting monocular visual inertial odometry datasets while driving. . The calibrated IMU generates 14 numbers at 100Hz: time, 3-axis accelerometer measurements, 3-axis gyroscope measurements, 3 Euler angles, and 4 quaternions. This VIO system is composed of a monocular camera, GoPro Hero 3, an IMU, BNO055, and a GPS, ANT-555. The sensors are mounted on a John Deere Gator™ Full-Size Crossover Utility Vehicle and are operated on an Intel NUC, a mini-size computer. The final dataset contains monocular images, IMU measurements, and GPS outputs.

This paper presents a navigation grade micro-electromechanical system (MEMS) inertial measurement unit (IMU) that was successfully applied for the first time in the Lobster-Eye X-ray Satellite in July 2020. A six-axis MEMS gyroscope redundant configuration is adopted in the unit to improve the performance through mutual calibration of a set of two-axis gyroscopes in the same direction. In the paper, a satisfactory precision of the gyroscope is achieved by customized and self-calibration gyroscopes whose parameters are adjusted at the expense of bandwidth and dynamics. According to the in-orbit measured data, the MEMS IMU provides an outstanding precision of better than 0.02 °/h (1σ) with excellent bias instability of 0.006 °/h and angle random walk (ARW) of around 0.003 °/h1/2. It is the highest precision MEMS IMU for commercial aerospace use ever publicly reported in the world to date.

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.

In the ultra-tight integration, the signal processing of GNSS receiver baseband can be enhanced by the aiding of INS information. With the purpose of improving the performance of ultra-tight integration with low-cost MEMS (Micro-Electro-Mechanical System) IMU (Inertial Measurement Unit), the paper explores a deeply coupled integration scheme of GNSS and the software defined IMU (SDI) based on vibratory MEMS rate gyroscope. The error of vibratory MEMS rate gyroscope demodulated by peak detection technique is interpreted in the gyroscope signal domain and the gyroscope demodulation parameter deviations are estimated by the integration filter and adjusted accordingly. The presented integration scheme is tested by an integrated navigation system which consists of a software defined GNSS receiver, a single-axis vibratory MEMS gyroscope GI-CVG-N2100A based SDI and an Xsens MEMS IMU. The single-axis vibratory MEMS gyroscope GI-CVG-N2100A in the test, is employed to replace one of the gyroscope axes of the Xsens MEMS IMU.

This dissertation presents a comprehensive approach to addressing the challenges associated with the control and navigation of Unmanned Aerial Vehicles (UAVs), specifically focusing on robustness under GPS-denied, turbulent, and dark indoor conditions with various disturbances. Initial efforts involved designing and theoretically validating Sliding Mode Controllers (SMC) with Disturbance Observers using MATLAB/SIMULINK, optimized using offline Particle Swarm Optimization (PSO). In order to evaluate their performance, a lightweight simulator was developed to simulate both quadcopter and hexacopter configurations under the PX4 architecture. This simulator served to validate and compare the optimized SMC against PSO-optimized PID controllers. The results of these comparisons demonstrated the superior adaptability of the SMC, especially in environments characterized by significant sensor noise and disturbances. Subsequent practical indoor tests were conducted with real quadcopters, which provided additional verification of the robustness and reliability of the enhanced control theory with the actual UAVs under dynamic disturbances. To further evaluate the compatibility and adaptability of the SMC, this dissertation establishes a framework for interfacing with high-performance STMH7 chips, serving as the UAV microcontroller unit (MCU). This framework facilitates the connection of various sensors and provides guidance for constructing different quadcopter frames. Moreover, it enables the comparison of different flight controllers on distinct air-frames. Concurrently, the optimized SMC allows for remote dynamic parameter tuning during flight. In pursuit of improved localization capacity, the dissertation also explores the integration of an Intel Realsense D435i camera with an Inertial Measurement Unit (IMU) for Visual-Inertial Odometry (VIO) fusion within the Robot Operating System (ROS) environment. This integration employs an onboard companion computer for real-time VIO estimation and flight command. The effectiveness of the VIO algorithm is verified through both simulations and real-world experiments, highlighting its ability to enhance UAV localization and navigation. Additionally, a visual inertial odometry method for UAV Simultaneous Localization and Mapping (vSLAM) is tested and validated in both simulated and real drone environments. Furthermore, the dissertation introduces an innovative hybrid filtered multi-directional radar inertial odometry solution denoted as Hybrid-MRIO. This Error State Extended Kalman Filter (ES-EKF) based Radar Inertial Odometry (RIO) approach is implemented within the open-source PX4 autopilot system, enhancing UAV navigation accuracy and performance, and serves as a robust navigation alternative, particularly in challenging environments where traditional navigation systems prove ineffective, such as dark or smoky conditions. This solution integrates data from multiple software synchronized high-resolution Frequency-Modulated Continuous-Wave (FMCW) mmWave radars with an IMU. A subsequent 4D (x, y, z, doppler) mmWave radar SLAM (rSLAM) system has been developed, including three modules: front-end, loop detection module, and back-end. In the front-end, radar ego-velocity is utilized for states estimation and dynamic object removal, and a point cloud registration-based approach known as APDGICP (Adaptive Probability Distribution-GICP) is employed for keyframe detection. The loop detection module utilizes the intensity scan context to identify potential loop closure candidates. In the back-end, a pose graph is constructed, integrating RIO estimated odometry and identified loop closures for better localization. To develop fully-autonomous UAVs with their decision making modules, this dissertation details the development of a custom AI-powered fully-autonomous quadcopter equipped with companion computers and stereo IR cameras. Additionally, this dissertation discusses a swarm of small Unmanned Aerial Systems (SUAS) interconnected through a software-defined communications network. These drones demonstrated their capacity to independently/collaboratively execute a wide range of tasks, including target search, detection, identification, classification, tracking, and following, both in simulated and real-world scenarios. The system exhibits advanced collision avoidance capabilities and the ability to strategically respond to dynamic scenarios, such as changes in target behavior, highlighting its effectiveness in managing dynamic and challenging situations without the need for continuous human intervention.--Author's abstract

Autonomous landing system is an important part of unmanned aerial vehicle (UAV) autonomous flight. Large fixed‐wing UAVs often consume more onboard space and computing resources. With the improvement of system integration and the reduction of high‐performance global navigation satellite system (GNSS) costs, real‐time kinematic (RTK) GNSS has been widely used in small‐scale and low‐cost fixed‐wing UAVs. This article uses less onboard hardware and ground facility requirements to design an autonomous taking off and landing system suitable for general fixed‐wing UAVs. A modified gradient descent attitude estimator, based on microelectromechanical system (MEMS) inertial measurement unit (IMU) and RTK GNSS, and a guidance method based on a tracking differentiator for using in a high dynamic environment are proposed, and circular path following and vertical transient simulations are performed. The simulation results show that this method can improve attitude estimation accuracy, motion control accuracy, and altitudinal transient quality. Finally, the actual flight test verifies its feasibility in the actual environment. The flight test results prove that it is feasible to implement the autonomous landing process of fixed‐wing UAV with the proposed system and method.

This paper describes the use of a low-cost standalone MEMS-IMU (micro-electromechanical system - inertial measurement unit) sensor system developed by the National Research Council Canada (NRC) for manoeuvre recognition in helicopters. The system records accelerations, angular rotation rates, magnetic flux, altitude, location and velocity through its IMU and GPS. The MEMS-IMU system was flown on the Bell 412 CH-146 Griffon helicopter in a series of scripted flights consisting of 60 manoeuvres and regimes from the helicopter's usage spectrum. A flight log recorded by passengers with detailed start and stop times and identification of the manoeuvres during flight was key information in the development of manoeuvre recognition models. Statistical tests to analyze the data diversity between each flight showed significant variability of the parameters from flight to flight. Because of this variability and because there was significant imbalance in the distribution of data for the individual manoeuvres, a pooled stratified sampling scheme was used to construct a representative training set for developing data-driven models. Different subsets of the MEMSIMU measurements were explored to exclude GPS and/or magnetometer readings. Even with the different subsets, the classifier results using stratified sampling show that very high overall classification accuracy can be obtained using the measurements from the standalone MEMS-IMU sensor system.

The procedure of determining the initial values of the attitude angles (pitch, roll, and heading) is known as the alignment. Also, it is essential to align an inertial system before the start of navigation. Unless the inertial system is not aligned with the vehicle, the information provided by MEMS (microelectromechanical system) sensors is not useful for navigating the vehicle. At the moment MEMS gyroscopes have poor characteristics and it’s necessary to develop specific algorithms in order to obtain the attitude information of the object. Most of the standard algorithms for the attitude estimation are not suitable when using MEMS inertial sensors. The wavelet technique, the Kalman filter, and the quaternion are not new in navigation data processing. But the joint use of those techniques for MEMS sensor data processing can give some new results. In this paper the performance of a developed algorithm for the attitude estimation using MEMS IMU (inertial measurement unit) is tested. The obtained results are compared with the attitude output of another commercial GPS/IMU device by Xsens. The impact of MEMS sensor measurement noises on an alignment process is analysed. Some recommendations for the Kalman filter algorithm tuning to decrease standard deviation of the attitude estimation are given.

To improve the accuracy and robustness of the inertial navigation systems (INS) for wheeled robots without adding additional component cost, we propose Wheel-INS, a complete dead reckoning (DR) solution based on a wheel-mounted microelectromechanical system (MEMS) inertial measurement unit (IMU) in this article. There are two major advantages of mounting IMU to the center of a non-steering wheel. Firstly, the gyroscope outputs can be used to calculate the wheel speed, so as to replace the traditional odometer to mitigate the error drift of INS. Secondly, with the rotation of the wheel, the constant bias error of the inertial sensor can be canceled to some extent. The installation scheme of the wheel-mounted MEMS IMU (Wheel-IMU), the system characteristics, and the DR error analysis are all discussed in this paper. Experimental results show that the maximum position drift of Wheel-INS in the horizontal plane is less than 1.8% of the total traveled distance, reduced by 23% compared to the conventional odometer-aided INS (ODO/INS). In addition, Wheel-INS outperforms ODO/INS because of its inherent immunity to constant bias error of gyroscopes. The source code and experimental datasets have been made available to the community ( <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/i2Nav-WHU/Wheel-INS</uri> ).

In the γ radioactive environment, high-energy photons induce degradation of the image sensor, which effects feature detection and tracking in the Visual Inertial Odometry (VIO) algorithm and deteriorates its localization performance. To address this issue, in this work, we propose a monocular VIO method using edge-based point features. To mitigate the effects of radiation noise, firstly, in the image preprocessing module, the median filter is used for real-time image denoising. Secondly, in the data association module, both Shi-Tomasi and edge-based point features are detected. The edge-based point feature is the endpoint or corner point in the salient edge map, which is more robust to radiation noise. Then, the bi-directional motion parallaxes and the RANdom SAmple Consensus (RANSAC) method are exploited to reject outliers. Finally, the point features measurements and Inertial Measurement Unit (IMU) pre-integration measurements are added into a tightly-coupled sliding window optimization VIO framework for localization estimation. The proposed method is verified by synthetic and real γ radioactive environment datasets. The experimental results show that the proposed method achieves more accurate and robust localization than the state-of-the-art VIO approaches in the γ radioactive environments.

Accurate motion estimation plays a crucial role in state estimation of an unmanned aerial vehicle (UAV). This is usually carried out by fusing the kinematics of an inertial measurement unit (IMU) with the video output of a camera. However, the accuracy of existing approaches is hindered by the discretization effect of the model even at a high IMU sampling rate. In order to improve the accuracy, we propose a new IMU motion integration model for the IMU kinematics in continuous time. The kinematics are modeled using a switched linear system. A closed-form discrete formulation is derived to compute the mean measurement, the covariance matrix, and the Jacobian matrix. Thus, it is more accurate and more efficient for online estimation of visual-inertial odometry (VIO), particularly when there is a high dynamic change in the agent's motion or the agent travels with high speed. The proposed IMU factor framework is evaluated using both real public datasets and indoor environment under different scenarios of motion capture. Our evaluation shows that the proposed framework outperforms the state-of-the-art VIO approach by up to 22.71% accuracy improvement on the EuRoc dataset and 38.15% accuracy improvement for motion estimation under the indoor environment.

Cameras and inertial measurement units are complementary sensors for ego-motion estimation and environment mapping. Their combination makes visual-inertial odometry (VIO) systems more accurate and robust. For globally consistent mapping, however, combining visual and inertial information is not straightforward. To estimate the motion and geometry with a set of images large baselines are required. Because of that, most systems operate on keyframes that have large time intervals between each other. Inertial data on the other hand quickly degrades with the duration of the intervals and after several seconds of integration, it typically contains only little useful information. In this letter, we propose to extract relevant information for visual-inertial mapping from visual-inertial odometry using non-linear factor recovery. We reconstruct a set of non-linear factors that make an optimal approximation of the information on the trajectory accumulated by VIO. To obtain a globally consistent map we combine these factors with loop-closing constraints using bundle adjustment. The VIO factors make the roll and pitch angles of the global map observable, and improve the robustness and the accuracy of the mapping. In experiments on a public benchmark, we demonstrate superior performance of our method over the state-of-the-art approaches.

In this paper, we present a wireless micro inertial measurement unit (IMU) with the smallest volume and weight requirements available at the moment. With a size of 22 mm × 14 mm × 4 mm (1.2 cm3), this IMU provides full control over the data of a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer. It meets the design prerequisites of a space-saving design and eliminates the need for hard-wired data communication, while still being competitive with state-of-the-art commercially available MEMS IMUs. A CC430 microcontroller sends the collected raw data to a base station wirelessly with a maximum sensor sample rate of 640 samples/s. Thereby, the IMU performance is optimized by moving data post processing to the base station. This development offers important features in portable applications with their significant size and weight requirements. Due to its small size, the IMU can be integrated into clothes or shoes for accurate position estimation in mobile applications and location-based services. We demonstrate the performance of the wireless micro IMU in a localization experiment where it is placed on a shoe for pedestrian tracking. With sensor data-fusion based on a Kalman filter combined with the zero velocity update, we can precisely track a person in an indoor area.

The performance of a commercial 6-axis (3-axis accelerometer and 3-axis gyroscope) MEMS inertial measurement unit (IMU) has been improved by a factor of >300x by utilizing a low-power ovenized microsystem. The IMU is thermally isolated from the external ambient by mounting it on a custom-designed micro-machined glass platform and packaging it in vacuum. In the present study, a microcontroller and voltage-controlled current source are assembled together with the thermally-isolated packaged IMU on a printed circuit board. The entire system is thermal-cycled over a temperature span from −40°C to 85°C. Both temperature control (i.e., ovenization) and compensation are used to reduce bias drift due to temperature change. The measured frequency drift of the IMU is improved by a factor of 950x and stabilized to ±2ppm, and the bias drift of the IMU is reduced to 60 °/hr for one of the gyroscope axes, and 1.7 mg for one of the accelerometer axes.