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.

Aiding MEMS IMU with building heading for indoor pedestrian navigation

Abstract: Heading drift error remains a problem in a standalone navigation system that uses only low cost MEMS IMU due to yaw error unobservability. This paper therefore proposes a shoe mounted IMU approach, integrated with ZUPT and building heading information in Kalman filter environment to reduce heading drift for pedestrian navigation application. There were no additional sensors used except MEMS IMU that contains accelerometers and gyros. Two trials; represented by regular and irregular walking trials, were undertaken in a typical public building. The results were then compared with HSGPS solution and IMU+ZUPT solution. Based on these trials, return position error of 0.1% from total distance travelled was achieved using a low cost MEMS IMU only.

Estimation of Gait Mechanics Based on Simulated and Measured IMU Data Using an Artificial Neural Network.

Abstract: Enhancement of activity is one major topic related to the aging society. Therefore, it is necessary to understand people's motion and identify possible risk factors during activity. Technology can be used to monitor motion patterns during daily life. Especially the use of artificial intelligence combined with wearable sensors can simplify measurement systems and might at some point replace the standard motion capturing using optical measurement technologies. Therefore, this study aims to analyze the estimation of 3D joint angles and joint moments of the lower limbs based on IMU data using a feedforward neural network. The dataset summarizes optical motion capture data of former studies and additional newly collected IMU data. Based on the optical data, the acceleration and angular rate of inertial sensors was simulated. The data was augmented by simulating different sensor positions and orientations. In this study, gait analysis was undertaken with 30 participants using a conventional motion capture set-up based on an optoelectronic system and force plates in parallel with a custom IMU system consisting of five sensors. A mean correlation coefficient of 0.85 for the joint angles and 0.95 for the joint moments was achieved. The RMSE for the joint angle prediction was smaller than 4.8° and the nRMSE for the joint moment prediction was below 13.0%. Especially in the sagittal motion plane good results could be achieved. As the measured dataset is rather small, data was synthesized to complement the measured data. The enlargement of the dataset improved the prediction of the joint angles. While size did not affect the joint moment prediction, the addition of noise to the dataset resulted in an improved prediction accuracy. This indicates that research on appropriate augmentation techniques for biomechanical data is useful to further improve machine learning applications.

Multi-DOF inertial MEMS: From gaming to dead reckoning

Abstract: This paper presents an overview of multi degrees-of-freedom (DOF) inertial MEMS in applications ranging from gaming to dead reckoning. Approaches to the implementation of high-performance inertial measurement units (IMU) are examined, focusing on challenges related to tri-axial gyroscope implementation. Benefits and tradeoffs of homogeneous multi-axis sensors are reviewed and contrasted with advances in single-chip integrated IMUs.

Robust UAV Relative Navigation With DGPS, INS, and Peer-to-Peer Radio Ranging

Abstract: This paper considers the fusion of carrier-phase differential GPS (CP-DGPS), peer-to-peer ranging radios, and low-cost inertial navigation systems (INS) for the application of relative navigation of small unmanned aerial vehicles (UAVs) in close formation-flight. A novel sensor fusion algorithm is presented that incorporates locally processed tightly coupled GPS/INS-based absolute navigation solutions from each UAV in a relative navigation filter that estimates the baseline separation using integer-fixed relative CP-DGPS and a set of peer-to-peer ranging radios. The robustness of the dynamic baseline estimation performance under conditions that are typically challenging for CP-DGPS alone, such as a high occurrence of phase breaks, poor satellite visibility/geometry due to extreme UAV attitude, and heightened multipath intensity, amongst others, is evaluated using Monte Carlo simulation trials. The simulation environment developed for this work combines a UAV formation flight control simulator with a GPS constellation simulator, stochastic models of the inertial measurement unit (IMU) sensor errors, and measurement noise of the ranging radios. The sensor fusion is shown to offer improved robustness for 3-D relative positioning in terms of 3-D residual sum of squares (RSS) accuracy and increased percentage of correctly fixed phase ambiguities. Moreover, baseline estimation performance is significantly improved during periods in which differential carrier phase ambiguities are unsuccessfully fixed.

Efficient vision-aided inertial navigation using a rolling-shutter camera with inaccurate timestamps

Abstract: Vision-aided inertial navigation techniques are described. In one example, a vision-aided inertial navigation system (VINS) comprises an image source to produce image data at a first set of time instances along a trajectory within a three-dimensional (3D) environment, wherein the image data captures features within the 3D environment at each of the first time instances. An inertial measurement unit (IMU) to produce IMU data for the VINS along the trajectory at a second set of time instances that is misaligned with the first set of time instances, wherein the IMU data indicates a motion of the VINS along the trajectory. A processing unit comprising an estimator that processes the IMU data and the image data to compute state estimates for 3D poses of the IMU at each of the first set of time instances and 3D poses of the image source at each of the second set of time instances along the trajectory. The estimator computes each of the poses for the image source as a linear interpolation from a subset of the poses for the IMU along the trajectory.