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.

UWB-Based Localization System Aided With Inertial Sensor for Underground Coal Mine Applications

Abstract: Robotic mining equipment plays an increasingly important role in the coal mining industry. Due to the complexity of the confined underground environment, available localization methods are limited, and restrict the development of coal mine robots (CMRs). Ultra-wideband (UWB) is a promising positioning sensor with high ranging accuracy. However, current applications about UWB positioning in coal mine focus mainly on position information, but rarely on orientation information. Positioning accuracy is often plagued by the loss of transmitted signals and multipath effects. In this paper, a pseudo-GPS positioning system in underground coal mine, composed by noisy UWB range measurements, is proposed to provide localization service for CMRs. An Error-State Kalman Filter (ESKF) is used for fusing measurements from the inertial measurement unit (IMU) and the established UWB positioning system. Then the complete six degree of freedom (6-DOF) state estimation can be realized. Meanwhile the biases of the IMU and the translation parameters of IMU w.r.t. UWB mobile node are also estimated online to adapt to long-term operation in harsh underground environments. In addition, an UWB anchor optimal deployment strategy is discussed to deploy UWB nodes appropriately in the laneway, and maintain realistic positioning accuracy for CMR in the meantime. A large number of field tests in different environments including the actual underground coal mine were conducted. The experimental results showed that our method could obtain the pose estimation performance close to the state-of-the-art lidar odometry approach that has been currently utilized in underground coal mine, providing robust and precise localization estimation for CMR applications.

LSTM-Based Zero-Velocity Detection for Robust Inertial Navigation

Abstract: We present a method to improve the accuracy of a zero-velocity-aided inertial navigation system (INS) by replacing the standard zero-velocity detector with a long short-term memory (LSTM) neural network. While existing threshold-based zero-velocity detectors are not robust to varying motion types, our learned model accurately detects stationary periods of the inertial measurement unit (IMU) despite changes in the motion of the user. Upon detection, zero-velocity pseudo-measurements are fused with a dead reckoning motion model in an extended Kalman filter (EKF). We demonstrate that our LSTM-based zero-velocity detector, used within a zero-velocity-aided INS, improves zero-velocity detection during human localization tasks. Consequently, localization accuracy is also improved. Our system is evaluated on more than 7.5 km of indoor pedestrian locomotion data, acquired from five different subjects. We show that 3D positioning error is reduced by over 34% compared to existing fixed-threshold zero-velocity detectors for walking, running, and stair climbing motions. Additionally, we demonstrate how our learned zero-velocity detector operates effectively during crawling and ladder climbing. Our system is calibration-free (no careful threshold-tuning is required) and operates consistently with differing users, IMU placements, and shoe types, while being compatible with any generic zero-velocity-aided INS.

Camera and inertial measurement unit integration with navigation data feedback for feature tracking

Abstract: A navigation device is provided herein comprising an inertial measurement unit (IMU), a camera, and a processor. The IMU provides an inertial measurement to the processor and the camera provides at least one image frame to the processor. The processor is configured to determine navigation data based on the inertial measurement and the at least one image frame, wherein at least one feature is extracted from the at least one image frame based on the navigation data.

Quantitative Measurement of Soil Erosion from Tls and Uav Data

Abstract: . Soil erosion is a major issue concerning crop land degradation. Understanding these complex erosion processes is necessary for effective soil conservation. Herein, high resolution modelling of relief changes caused by run-off from precipitation events is an essential research matter. For non-invasive field measurements the combination of unmanned airborne vehicle (UAV) image data and terrestrial laser scanning (TLS) may be especially suitable. The study's objective is to measure high precision digital terrain models (DTM) of the soil surface at two selected research areas with the extent of at least 500 square meters. The used UAV is integrated with GPS and inertial measurement unit (IMU). Furthermore, an active stabilizing camera mount equipped with a customary compact camera is implemented. For multi-temporal comparison of measured soil surfaces and for aligning UAV and TLS data a stable local reference system consisting of signalized points is defined by total station measurements. Two different software packages are applied for DTM generation from UAV images and compared to the corresponding DTM captured by TLS. Differences between the point clouds are minimal six millimeters and generally within TLS accuracy range. First multi-temporal comparisons are made and illustrate interesting surface changes.

Prediction of lower limb joint angles and moments during gait using artificial neural networks

Abstract: In recent years, gait analysis outside the laboratory attracts more and more attention in clinical applications as well as in life sciences. Wearable sensors such as inertial sensors show high potential in these applications. Unfortunately, they can only measure kinematic motions patterns indirectly and the outcome is currently jeopardized by measurement discrepancies compared with the gold standard of optical motion tracking. The aim of this study was to overcome the limitation of measurement discrepancies and the missing information on kinetic motion parameters using a machine learning application based on artificial neural networks. For this purpose, inertial sensor data—linear acceleration and angular rate—was simulated from a database of optical motion tracking data and used as input for a feedforward and long short-term memory neural network to predict the joint angles and moments of the lower limbs during gait. Both networks achieved mean correlation coefficients higher than 0.80 in the minor motion planes, and correlation coefficients higher than 0.98 in the sagittal plane. These results encourage further applications of artificial intelligence to support gait analysis.