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.

LINS: A Lidar-Inertial State Estimator for Robust and Efficient Navigation

Abstract: We present LINS, a lightweight lidar-inertial state estimator, for real-time ego-motion estimation. The proposed method enables robust and efficient navigation for ground vehicles in challenging environments, such as feature-less scenes, via fusing a 6-axis IMU and a 3D lidar in a tightly-coupled scheme. An iterated error-state Kalman filter (ESKF) is designed to correct the estimated state recursively by generating new feature correspondences in each iteration, and to keep the system computationally tractable. Moreover, we use a robocentric formulation that represents the state in a moving local frame in order to prevent filter divergence in a long run. To validate robustness and generalizability, extensive experiments are performed in various scenarios. Experimental results indicate that LINS offers comparable performance with the state-of-the-art lidar-inertial odometry in terms of stability and accuracy and has order-of-magnitude improvement in speed.

Interruption-free hand-held positioning method and system thereof

Abstract: An interruption-free hand-held positioning method and system, carried by a person, includes an inertial measurement unit, a north finder, a velocity producer, a positioning assistant, a navigation processor, a wireless communication device, and a display device and map database. Output signals of the inertial measurement unit, the velocity producer, the positioning assistant, and the north finder are processed to obtain highly accurate position measurements of the person. The user's position information can be exchanged with other users through the wireless communication device, and the location and surrounding information can be displayed on the display device by accessing a map database with the person position information.

IMU-Based Classification of Parkinson's Disease From Gait: A Sensitivity Analysis on Sensor Location and Feature Selection

Abstract: Inertial measurement units (IMUs) have a long-lasting popularity in a variety of industrial applications from navigation systems to guidance and robotics. Their use in clinical practice is now becoming more common, thanks to miniaturization and the ability to integrate on-board computational and decision-support features. IMU-based gait analysis is a paradigm of this evolving process, and in this study its use for the assessment of Parkinson's disease (PD) is comprehensively analyzed. Data coming from 25 individuals with different levels of PD symptoms severity and an equal number of age-matched healthy individuals were included into a set of 6 different machine learning (ML) techniques, processing 18 different configurations of gait parameters taken from 8 IMU sensors. Classification accuracy was calculated for each configuration and ML technique, adding two meta-classifiers based on the results obtained from all individual techniques through majority of voting, with two different weighting schemes. Average classification accuracy ranged between 63% and 80% among classifiers and increased up to 96% for one meta-classifier configuration. Configurations based on a statistical preselection process showed the highest average classification accuracy. When reducing the number of sensors, features based on the joint range of motion were more accurate than those based on spatio-temporal parameters. In particular, best results were obtained with the knee range of motion, calculated with four IMUs, placed bilaterally. The obtained findings provide data-driven evidence on which combination of sensor configurations and classification methods to be used during IMU-based gait analysis to grade the severity level of PD.

Advanced tracking through efficient image processing and visual-inertial sensor fusion

Abstract: We present a new visual-inertial tracking device for augmented and virtual reality applications. The paper addresses two fundamental issues of such systems. The first one concerns the definition and modelling of the sensor fusion. Much work has been done in this area and several models for exploiting the data of the gyroscopes and linear accelerometers have been proposed. However, the respective advantages of each model and in particular the benefits of the integration of the accelerometer data in the filter are still unclear. The paper therefore provides an evaluation of different models with special investigation of the effects of using accelerometers on the tracking performance. The second contribution is about the development of an image processing approach that does not require special landmarks but uses natural features. Our solution relies on a 3D model of the scene that enables to predict the appearances of the features by rendering the model using the prediction data of the sensor fusion filter. The feature localisation is robust and accurate mainly because local lighting is also estimated. The final system is evaluated with help of ground-truth and real data. High stability and accuracy is demonstrated also for large environments.