Chip-scale IMU using folded-mems approach

In this paper, we propose a novel wafer-level approach for fabrication and 3-D integration of MEMS devices in miniature multi-axis assemblies of inertial, acoustic, and optical sensors. The approach is based on simultaneous fabrication of all sensors on the same substrate connected by flexible electrical interconnects, mechanical hinges and latches. A multi-axis sensor system is then obtained by folding the fabricated structures into 3-D cubes, pyramids, or other rigid shapes, and subsequently micro-welded. In the current work, we demonstrate feasibility of the folded cube approach for creation of miniature MEMS IMU with <1 cm3 volume. Design of the IMU consists of a folded cube or pyramid backbone structure with micromachined accelerometers and gyroscopes on its sidewalls. Silicon-on-insulator (SOI) wafers are used as a substrate for fabrication of both the inertial sensors and the folded backbone structure. Fabrication of the sensors consists of lithography, deep reactive ion etching (DRIE), and HF acid release of the inertial proof masses. Flexible polymer hinges connecting faces of the folded structures are defined on the same substrate and incorporate electrical interconnects. To provide rigidity to the assembled 3-D structure, interlocking silicon latches are fabricated along the edges of each sidewall, which are silicon-to-silicon welded after assembly. The approach allows for creating miniature multi-axis sensor systems without compromising performance of individual sensors. Gyroscopes integrated in the current folded cube IMU have experimentally demonstrated 3-dB bandwidth of 250 Hz and angle random walk (ARW) below 0.1 (deg/s)/rt-Hz in atmospheric pressure. Measured uncompensated temperature coefficients of gyroscope bias and scale factor were 313 (deg/h)/degC and 351ppm/degC, respectively. Accelerometers on the cube have been characterized, yielding a noise floor of 570μG/√Hz. Sensitivity of the accelerometers is measured at 40mV/G with a bandwidth of 100 Hz.

Obtaining accurate floor plans of buildings is critical for optimising indoor geographic information system (GIS) applications. In this paper, we present a room measurement tool that utilises a smartphone equipped with an ultrasonic sensor. To take measurements, users complete a lap along the walls of all of the rooms. Then the tool accurately estimates the shape and size of them by tracking the walking paths of users and measuring the distance from the path to the walls with ultrasonic sensors. To track walking paths, we utilise inertial sensors embedded in the smartphone to estimate walking steps and turns, and the ultrasonic sensors to estimate the stride length when walking toward the wall. To account for such adjacent objects as bookshelves that decrease the accuracy of room size estimation, we used a mixed Gaussian filter. Our experimental results show that our tool considerably improved the estimation accuracy of the room shape and size.

This paper describes the development of a “smart rock,” an instrumented device for the study of debris flows, which is often triggered by earthquakes, heavy rain events, and rising groundwater conditions. Debris flows are very destructive forms of landslide consisting of a mixture of rocks, saturated soil, and debris typically flowing at high rates of speed and over long distances. In an effort to better understand the mechanics of debris flows, the smart rock was developed with a sensor package to be used in U.S. Geological Survey experiments at their flume facility in the Willamette National Forest, OR. The instrumented rock contains an inertial measurement unit (IMU) to measure acceleration and rotation rate about three body fixed axes, and two pressure sensors to measure pore water pressure. The sensors provide information about the movement of the rock and pore water pressures within a debris flow. One of the objectives of the sensor package is to use this information to track the position of a particle in the flow with an accuracy of 1 m over the course of 10 s. Calculation of position using the IMU requires the use of strapdown inertial navigation equations. Unfortunately, noise and bias in the rotation rate sensor introduce significant error in the position calculation. The results from one of the USGS debris flow experiments using the smart rock show that an ad hoc filtering method on the IMU data provides a rough estimate of the rock position in the flume, but far from the desired level of accuracy. Pressure and velocity recorded by the smart rock, while comparable to those measured by the USGS during the debris flow test, cannot be verified. Position accuracy can only be improved by using a better IMU and obtaining known rock positions versus time during the debris flow. Based on the results of this work, it is hoped that improved technology will result in a smart rock that can successfully provide useful and insightful debris flow data.

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.

Nowadays, multi-sensor fusion technology is a fundamental prerequisite to achieve highly autonomous robots and robustness. Many studies have been conducted, such as visual–inertial odometry (VIO), integrated navigation, and LiDAR–inertial odometry. Typically, for VIO, gratifying results have been achieved, ascribing to the complementary sensing capabilities of inertial measurement units (IMUs) and cameras. However, this work mainly focuses on the fusion of visual and inertial data, while the IMU error is less considered, especially for low-cost or poorly calibrated microelectromechanical system (MEMS) IMU. Such errors may have a significant effect on the VIO performance. In this study, we compensated for the IMU using camera assistance. The key characteristic of the method is that we optimize the compensation parameters (scale factor) from coarse to fine by combining the time domain with the frequency domain. The proposed method is to use the time-domain and frequency-domain optimization to suppress large noise in the dynamic calibration process of the extremely low-cost sensor platform. The effectiveness of this method is validated through experiments and simulations. The minimal calibration error (0.46%) is commensurate with the advanced work. By feeding the compensated IMU into the VIO algorithm, the localization accuracy is improved by 9% to 15%. This method improves the performance in the VIO algorithm, which is equipped with the low-cost or poorly calibrated MEMS IMU and reduces the hardware and deployment costs of the system.

Development of an open-source algorithm based on inertial measurement units (IMU) of a smartphone to detect cattle grass intake and ruminating behaviors

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 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.

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.

This paper proposes a novel approach for estimating the navigation states of land vehicles in GNSS denied environment by integrating low-cost multiple ultrasonic sensors with the Inertial Measurement Unit (IMU) using Extended Kalman Filter (EKF). These multiple ultrasonic sensors act as an aiding by providing the vehicle forward velocity to limit the INS large drift during GNSS signal outages. Ultrasonic sensors are installed on the left and right rear wheels to measure the range difference between the sensor and the spokes of the wheel to determine the angular velocity and then determine the vehicle forward velocity. As the ultrasonic raw data is contaminated with outliers and noise, outliers' removal is applied, and a moving average filter is used to reduce the noise. Two experimental road tests were performed for low velocity (30 km/hr) and moderate vehicle velocity (50 km/hr). Ultrasonic sensors were integrated with GNSS/INS in loosely coupled integration scheme through EKF. The Root Mean Square Error (RMSE) of the velocity estimated by the ultrasonic sensors was 0.28 m/sec. Moreover, the position RMSE enhanced from 101.18 meters for the case of INS standalone navigation solution to 5.07 meters when INS integrated with ultrasonic sensors for GNSS signal outage of 60 seconds in the first test. The RMSE of the position is decreased to 17.99 meters in case of ultrasonic/INS integration navigation solution compared to INS standalone solution with RMSE of 72.43 meters for an outage of 60 seconds in the second test. The proposed multiple ultrasonic system provides the land vehicle navigation solution with forward velocity update with higher accuracy and data rate than the velocity provided by regular odometer of On-Board Diagnostics (OBD II).

Robotics technology has developed rapidly and applied to various fields of work that cannot be done by humans. Many forms and types of robots used include the hexapod robot. The purpose of this research is to regulate the direction of the hexapod robot’s motion and be able to avoid obstacles to maintain the stability of its motion using a PID control system. In establishing a good control system, various types of sensors are used, those are ultrasonic and inertial measurement unit sensors. The shape of the hexapod robot is made to be integrated with the robot's legs. Each leg of the robot consists of 3 joints, joined by a servo motor. There are 18 servo motors. Eight ultrasonic sensors are used to detect surrounding objects, run well, and ability to avoid obstacles. The main orientation of the hexapod robot motion is to move forward, but if there are obstacles, it will find a solution to turn using ultrasonic sensors. An inertial measurement unit sensor is used to maintain the robot’s body’s stability.

Due to the complementary nature of inertial measurement units (IMUs) and GNSS, lots of research to couple these two technologies date back to the early days of GPS, primarily aiming at bridging GPS outages during absolute positioning. A precise and robust determination of all six degrees of freedom (6DoF: x, y, z, pitch, roll, heading) is essential for numerous aerial and land-based applications, such as machine guidance, mobile mapping systems, UAVs and precision farming. Determining the 6DoF in a robust manner is not possible with a single system – GNSS or IMU – when the trajectory of the movement is unpredictable or when the system needs to work stable over a longer static period as well. In particular, navigation systems aided by cameras (visual inertial systems – VIS), LiDAR or scanning benefit from precise attitude information. Yet, systems that require high-precision attitude information in real time are based on high-grade, expensive IMUs or multiple GNSS antennas, which are typically not portable. Existing smaller, lower-cost attitude systems either require time-consuming calibration procedures or do not fulfil the requirements on robustness and reliability in high-precision, real-time applications. This paper analyses the potential and limits of a novel technology that is used in the Leica GS18 T GNSS smart antenna to automatically compensate the position errors of a tilted survey pole by continuously determining the 6DoF. The sensor fusion of a high-precision, multi-GNSS smart antenna with an industrial-grade MEMS IMU allows the determination of pitch and roll with an accuracy of better than 0.2 degrees. The sensor heading with respect to true north is achievable instantaneously through metre-level movements, where the associated accuracy reaches 1 degree. Due to an advanced production procedure, the GS18 T does not require any on-site calibrations and works out of the box. Furthermore, using an innovative way of determining the sensor’s attitude based on velocity, the system is entirely immune to magnetic disturbances during operation. Based on the 6DoF and the length of the pole, the antenna phase centre coordinates are reduced to the pole tip without noticeably increasing the position error budget. GNSS RTK can now be applied in more restrictive situations with enhanced productivity by simply forgetting the level bubble. Since knowledge of the uncertainty and reliability of position and attitude is crucial when the highest accuracy is demanded, the GS18 T provides the user with coordinate quality estimates indicating the expected position and attitude uncertainty. In this study, accurate laser tracker measurements are used as reference to assess the quality of the 6DoF delivered by the GS18 T. Representative tests were carried out in static and kinematic application scenarios to evaluate the performance of the position and attitude determination with respect to accuracy, reliability and robustness. The test results also show that the high requirements for surveying-grade applications have enormous future potential in land-based applications or other applications where positioning and attitude information can be mission critical. For example, image-based reality capturing technologies, such as point cloud creation with UAVs or terrestrial mobile mapping systems, require precise 6DoF for their cameras and sensors. These applications and industries are constantly looking to drive costs and size down. The presented technology of a robust and reliable determination of the 6DoF can be one answer to save costs, space and complexity in such systems.

The NewSpace landscape for microlaunchers is highly competitive. The cost is one of the main drivers in this competition. Several microlauncher companies have decided to outsource the GNC sub-system to external providers, procuring low-cost commercial-of-the-shelf solutions. Lately, Astos has been involved in several activities related with GNC for (micro-)launchers. Astos’ work has been focused on providing GNC development tools and flexible and easily reconfigurable GNC software that can be applied to a wide range of launch vehicles. Navigation is one of the main drivers for launcher orbital injection accuracy and it is also one of the main drivers for cost in the GNC sub-system. Usually, this cost is driven by the Inertial Navigation System (INS) or Inertial Measurement Unit (IMU), in which expensive technologies are employed such as ring-laser gyroscopes. In traditional inertial propagation algorithms, errors are accumulated due to integration resulting in a steady divergence of the position and attitude estimate. Therefore, the performance of the navigation mainly depends on the performance of the IMU. This makes an investment into a highly accurate inertial sensors, especially the gyroscope, necessary to meet the navigation requirements. Lately, the use of hybrid navigation solutions that fuse the inertial measurements with Global Navigation Satellite Systems (GNSS) measurements has diminished, in some degree, the reliance of the navigation algorithm on the IMU performance. Although this is true for translational states, as position and velocity, the same cannot be said with the same degree of validity for attitude determination and, more specifically, for roll angle estimation. One possible way to reduce the cost of inertial sensors is by using microelectromechanical systems (MEMS). The advantages of using this technology, apart from the large reduction in cost, are the significant saves in mass and power required to operate these sensors. The disadvantage is clearly the lower performance, especially in high-vibration environments such as encountered in launcher vehicles. Lately, efforts to improve the performance of MEMS navigation solutions were driven by the demand of industries such as the wearables and unmanned aerial systems. This led to the development of solutions that utilize multiple low-cost inertial measurement units. By that, the hardware cost is not gravely increased in exchange for higher navigation accuracy, reliability, and redundancy. In this paper, a navigation algorithm is proposed that incorporates the measurements from multiple low-grade MEMS IMUs. The algorithm implements parallel Extended Kalman Filters (EKF), one for each IMU, combining the inertial measurements with the position, velocity, and time solutions provided by a single GNSS receiver. The algorithm has a loosely coupled architecture and the inertial propagation is regularly reset using the fused estimates of the vehicle’s state and IMU biases (closed-loop implementation). The navigation solution, providing estimates of position, velocity, and attitude, is then a combination of the individual EKF results through weighted means based on the covariance of the individual estimates. This formulation utilizing parallel filters is called in the literature as Federated Kalman Filter architecture. An analysis will be presented relating the performance of the navigation architecture with the number of sensors used and their distribution on the launch vehicle’s body. The assessment is performed with relation to a baseline navigation architecture also implementing a loosely coupled, closed-loop, hybrid structure but employing a single low-grade IMU. The analysis will determine the most promising sensor constellation, based on navigation performance, cost and implementation complexity. The selected multiple IMU solution is then compared with two different navigation architectures: Strapdown inertial propagation utilizing a single high-grade IMU; and hybrid navigation scheme implementing a loosely coupled, closed-loop architecture with a medium grade IMU. The performance evaluation is conducted utilizing simulated measurements retrieved from high-fidelity 6 degrees-of-freedom non-linear simulator of a representative microlauncher scenario. The simulators’ dynamics, kinematics, environment, and sensor modelling are provided by the ASTOS tool. Astos intends to further study the feasibility and performance of the proposed Inertial/GNSS hybrid navigation architecture with multiple IMUs. In the roadmap for raising the algorithm’s technology readiness level, more advanced simulations are foreseen, with improved sensor error modelling and previously unmodelled dynamical effects such as flexibility and sloshing. Additionally, processor- and hardware-in-the-loop tests with real sensor data are planned.

To enhance the inertial sensors performance, accurate characterization as well as modeling of the corresponding stochastic errors are required. In addition, accurate position and orientation information can be achieved when integrating inertial sensors with other position fix systems, such as Global Navigation Satellite Systems (GNSS). This paper investigates the Generalized Method of Wavelet Moments (GMWM), as a Wavelet Variance (WV)-based approach, for the stochastic characterization and model identification of the stochastic errors related to low-cost MEMS-based inertial sensor errors. Raw inertial sensor measurements were collected using MTi-G-710 MEMS Inertial Measurement Unit (IMU) where the GMWM was utilized for estimating the coefficients of different random processes associated with inertial sensor residuals. Results demonstrated that highly complicated error model structures are identified for the tested IMU, which contradicts the common assumption of modeling such errors as a single 1st order Gauss-Markov (GM) random process. In addition, the results showed that the GMWM is an efficient framework that could be used for estimating the parameters associated with complex structured random processes with the advantage of correlated residual characterization and identification.