Inertial measurement unit

About: Inertial measurement unit is a research topic. Over the lifetime, 13326 publications have been published within this topic receiving 189083 citations. The topic is also known as: IMU.

Integration of MEMS Inertial and Pressure Sensors for Vertical Trajectory Determination

Abstract: Integration of a low-cost global positioning system (GPS) with a microelectromechanical system-based inertial measurement unit (MEMS-IMU) is a widely used method that takes advantage of the individual superiority of each system to get a more accurate and robust navigation performance. However, because of poor observations as well as multipath effects, the GPS has low accuracy in the vertical direction. As a result, the navigation accuracy even in an integrated GPS/MEMS-IMU system is more challenged in the vertical direction than the horizontal direction. To overcome this problem, this paper investigates the integration of a MEMS barometric pressure sensor with the MEMS-IMU for vertical position/velocity tracking without the GPS that has applications in sports. A cascaded two-step Kalman filter consisting of separate orientation and position/velocity subsystems is proposed for this integration. Slow human movements in addition to more rapid sport activities such as vertical and step-down jumps can be tracked using the proposed algorithm. The height-tracking performance is benchmarked against a reference camera-based motion-tracking system and an error analysis is performed. The experimental results show that the vertical trajectory tracking error is less than 28.1 cm. For the determination of jump vertical height/drop, the proposed algorithm has an error of less than 5.8 cm.

IONet: Learning to Cure the Curse of Drift in Inertial Odometry

Abstract: Inertial sensors play a pivotal role in indoor localization, which in turn lays the foundation for pervasive personal applications However, low-cost inertial sensors, as commonly found in smartphones, are plagued by bias and noise, which leads to unbounded growth in error when accelerations are double integrated to obtain displacement Small errors in state estimation propagate to make odometry virtually unusable in a matter of seconds We propose to break the cycle of continuous integration, and instead segment inertial data into independent windows The challenge becomes estimating the latent states of each window, such as velocity and orientation, as these are not directly observable from sensor data We demonstrate how to formulate this as an optimization problem, and show how deep recurrent neural networks can yield highly accurate trajectories, outperforming state-of-the-art shallow techniques, on a wide range of tests and attachments In particular, we demonstrate that IONet can generalize to estimate odometry for non-periodic motion, such as a shopping trolley or baby-stroller, an extremely challenging task for existing techniques

Time Synchronization Error and Calibration in Integrated GPS/INS Systems

Abstract: The necessity for the precise time synchronization of measurement data from multiple sensors is widely recognized in the field of global positioning system/inertial navigation system (GPS/INS) integration. Having precise time synchronization is critical for achieving high data fusion performance. The limitations and advantages of various time synchronization scenarios and existing solutions are investigated in this paper. A criterion for evaluating synchronization accuracy requirements is derived on the basis of a comparison of the Kalman filter innovation series and the platform dynamics. An innovative time synchronization solution using a counter and two latching registers is proposed. The proposed solution has been implemented with off-the-shelf components and tested. The resolution and accuracy analysis shows that the proposed solution can achieve a time synchronization accuracy of 0.1 ms if INS can provide a hard-wired timing signal. A synchronization accuracy of 2 ms was achieved when the test system was used to synchronize a low-grade micro-electromechanical inertial measurement unit (IMU), which has only an RS-232 data output interface.

TLIO: Tight Learned Inertial Odometry

Abstract: In this letter we propose a tightly-coupled Extended Kalman Filter framework for IMU-only state estimation. Strap-down IMU measurements provide relative state estimates based on IMU kinematic motion model. However the integration of measurements is sensitive to sensor bias and noise, causing significant drift within seconds. Recent research by Yan et al. (RoNIN) and Chen et al. (IONet) showed the capability of using trained neural networks to obtain accurate 2D displacement estimates from segments of IMU data and obtained good position estimates from concatenating them. This letter demonstrates a network that regresses 3D displacement estimates and its uncertainty, giving us the ability to tightly fuse the relative state measurement into a stochastic cloning EKF to solve for pose, velocity and sensor biases. We show that our network, trained with pedestrian data from a headset, can produce statistically consistent measurement and uncertainty to be used as the update step in the filter, and the tightly-coupled system outperforms velocity integration approaches in position estimates, and AHRS attitude filter in orientation estimates. Video materials and code can be found on our project page:http://cathias.github.io/TLIO/.

A scheme for improving the performance of a gyroscope-free inertial measurement unit

Abstract: A gyroscope-free inertial measurement unit (GF-IMU) uses only accelerometers to compute specific force and angular rate. It is a low-cost inertial system, but its measurement error diverges at a rate that is an order faster than that of a conventional inertial system equipped with gyroscopes. In this paper, a redundant accelerometer-aided gyroscope-free IMU is designed to achieve a stable and bounded system. In this system, a three-axis accelerometer is used in addition to a set of six accelerometers that is typical in a conventional gyroscope-free IMU. The linear error dynamics of this aided gyroscope-free IMU is derived, and the effects of accelerometer errors are analyzed.