Showing papers on "Inertial reference unit published in 2014"

High-fidelity sensor modeling and self-calibration in vision-aided inertial navigation

Abstract: In this paper, we propose a high-precision pose estimation algorithm for systems equipped with low-cost inertial sensors and rolling-shutter cameras. The key characteristic of the proposed method is that it performs online self-calibration of the camera and the IMU, using detailed models for both sensors and for their relative configuration. Specifically, the estimated parameters include the camera intrinsics (focal length, principal point, and lens distortion), the readout time of the rolling-shutter sensor, the IMU’s biases, scale factors, axis misalignment, and g-sensitivity, the spatial configuration between the camera and IMU, as well as the time offset between the timestamps of the camera and IMU. An additional contribution of this work is a novel method for processing the measurements of the rolling-shutter camera, which employs an approximate representation of the estimation errors, instead of the state itself. We demonstrate, in both simulation tests and real-world experiments, that the proposed approach is able to accurately calibrate all the considered parameters in real time, and leads to significantly improved estimation precision compared to existing approaches.

A multi-position self-calibration method for dual-axis rotational inertial navigation system

Abstract: In order to compensate errors of inertial measurement unit which is the core of rotational inertial navigation system, self-calibration is utilized as an effective way to reduce navigation error. Error model of navigation solution and initial alignment is used to establish the relationship between navigation errors and inertial measurement unit (IMU) errors. A second order damper is added to the vertical velocity channel to suppress the divergence and then the vertical velocity error can be regarded as an effective observation to estimate the error parameters. Since the accuracy of the self-calibration method is susceptible to the positioning error of gimbals, total least squares (TLS) method is utilized in identification of the error parameters. Experimental results show that all of the twenty-one error parameters can be estimated with the proposed rotation scheme. Compared to least squares (LS) method, TLS method can improve the position accuracy of 8 h by 46.2%.

An open-source multi inertial measurement unit (MIMU) platform

Abstract: An open-source low-cost multi inertial measurement unit (MIMU) systems platform is presented. First, the layout and system architecture of the platform, as well as the novel communication interface used to simultaneously communicate with the 18 IMUs in the platform are described. Thereafter, the potential gains of using a MIMU system are described and discussed. Finally, the error characteristics of the platform, when stationary, are illustrated using Allan variance plots.

An historical perspective on inertial navigation systems

Abstract: Inertial navigation provides a unique ability to know where one has been, where one is currently, and where one is going, given only a starting position. The laws of physics permit the sensing of dynamic motion without external information, making inertial systems impervious to jamming, masking, or spoofing. Measurements of six degrees of freedom are required - three linear accelerations, and three angular rates - to fully propagate the velocity, position, and orientation of the system. The first inertial sensors are traced to the early 19th century and specialized inertial guidance systems appeared in the 1940s, yet inertial navigation systems did not become commonplace until the 1960s. This is largely due to the fact that requirements for navigation accuracy inertial sensors - accelerometers and gyroscopes - are very challenging. In the past fifty years, significant evolutionary and revolutionary changes have taken place in the designs of inertial sensors and systems. These include the progression from fluid-filled to dry instruments and the transition from mechanically complex stabilized inertial platforms to computationally intensive strapdown systems. Gyroscopes have evolved from large mechanical devices to highly refined precision mechanical sensors. Optical rotation sensors such as the ring laser gyro and the fiber optic gyro have enabled new system designs and capabilities. Coriolis vibratory gyroscopes such as the hemispherical resonator gyro are capable of extreme accuracy and reliability; new opportunities for miniaturizing these types of sensors will lead to new classes of accuracy for inertial navigation systems. Advanced gyroscope technologies such as the nuclear magnetic resonance gyroscope which uses atomic spin to detect rotation have already been demonstrated to achieve navigation accuracy requirements. Cold atom technologies may also provide the opportunity for very high accuracy accelerometers and gyroscopes in the future. Inertial navigation technologies and applications of the past, present, and future are discussed.

Miniature Low-Power Inertial Sensors: Promising Technology for Implantable Motion Capture Systems

Abstract: Inertial and magnetic sensors are valuable for untethered, self-contained human movement analysis. Very recently, complete integration of inertial sensors, magnetic sensors, and processing into single packages, has resulted in miniature, low power devices that could feasibly be employed in an implantable motion capture system. We developed a wearable sensor system based on a commercially available system-in-package inertial and magnetic sensor. We characterized the accuracy of the system in measuring 3-D orientation—with and without magnetometer-based heading compensation—relative to a research grade optical motion capture system. The root mean square error was less than 4 $^{\circ}$ in dynamic and static conditions about all axes. Using four sensors, recording from seven degrees-of-freedom of the upper limb (shoulder, elbow, wrist) was demonstrated in one subject during reaching motions. Very high correlation and low error was found across all joints relative to the optical motion capture system. Findings were similar to previous publications using inertial sensors, but at a fraction of the power consumption and size of the sensors. Such ultra-small, low power sensors provide exciting new avenues for movement monitoring for various movement disorders, movement-based command interfaces for assistive devices, and implementation of kinematic feedback systems for assistive interventions like functional electrical stimulation.

Aligning the Forces-Eliminating the Misalignments in IMU Arrays

Abstract: Ultralow-cost single-chip inertial measurement units (IMUs) combined into IMU arrays are opening up new possibilities for inertial sensing. However, to make these systems practical for researchers, ...

Pedestrian tracking using an IMU array

Abstract: Ubiquitous and accurate tracking of pedestrians are an enabler for a large range of emerging and envisioned services and capabilites. To track pedestrians in prevailing indoor environments, inertial measurement units (IMUs) may be used to implement foot-mounted inertial navigation. Today emerging ultra-low-cost IMUs are taking a leading role in the advancement of the IMU performance-to-cost boundary. Unfortunately, the performance of these IMUs are still insufficient to allow extended stand-alone tracking. However, the size, price, and power consumption of single-chip ultra-low-cost IMUs makes it possible to combine multiple IMUs on a single PCB, creating an IMU array. The feasibility of such hardware has recently been demonstrated. On the other hand, the actual gain of using such multi-IMU systems in the pedestrian tracking application is unclear. Therefore, based on an in-house developed IMU array, in the article we demonstrate that foot-mounted inertial navigation with an IMU array is indeed possible and benefitial. The error characteristics of the setup and different ways of combining the inertial measurements are studied and directions for further research are given.

Calibration of Miniature Inertial and Magnetic Sensor Units for Robust Attitude Estimation

Abstract: Attitude estimation from miniature inertial and magnetic sensors has been used in a wide variety of applications, ranging from virtual reality, underwater vehicles, handheld navigation devices, to biomotion analysis However, appropriate sensor calibrations for accurate sensor measurements are essential to the performance of attitude estimation algorithms In this paper, we present a robust sensor calibration method for accurate attitude estimation from three-axis accelerometers, gyroscopes, and magnetometer measurements The proposed calibration method only requires a simple pan-tilt unit A unified sensor model for inertial and magnetic sensors is used to convert the sensor readings to physical quantities in metric units Based on the sensor model, a cost function is constructed, and a two-step iterative algorithm is then proposed to calibrate the inertial sensors Due to the difficulties of acquiring the ground-truth of the Earth magnetic field, a simplified pseudomagnetometer calibration method is also presented based on an ellipsoid fitting algorithm The calibration method is then applied to our sensor nodes, and the good performance of the orientation estimation has illustrated the effectiveness of the proposed sensor calibration method

Extrinsic parameter calibration of a vision-aided inertial navigation system

Abstract: This disclosure describes various techniques for use within a vision-aided inertial navigation system (VINS). A VINS comprises an image source to produce image data comprising a plurality of images, and an inertial measurement unit (IMU) to produce IMU data indicative of a motion of the vision-aided inertial navigation system while producing the image data, wherein the image data captures features of an external calibration target that is not aligned with gravity. The VINS further includes a processing unit comprising an estimator that processes the IMU data and the image data to compute calibration parameters for the VINS concurrently with computation of a roll and pitch of the calibration target, wherein the calibration parameters define relative positions and orientations of the IMU and the image source of the vision-aided inertial navigation system.

The Compensation Effects of Gyros' Stochastic Errors in a Rotational Inertial Navigation System

Abstract: The errors of an inertial navigation system (INS) in response to gyros' errors can be effectively reduced by the rotation technique, which is a commonly used method to improve an INS's accuracy. A gyro's error consists of a deterministic contribution and a stochastic contribution. The compensation effects of gyros' deterministic errors are clear now, but the compensation effects of gyros' stochastic errors are as yet unknown. However, the compensation effects are always needed in a rotational inertial navigation system's (RINS) error analysis and optimization study. In this paper, the compensation effects of gyros' stochastic errors, which are modelled as a Gaussian white (GW) noise plus a first-order Markov process, are analysed and the specific formulae are derived. During the research, the responses of an INS's and a RINS's position error equations to gyros' stochastic errors are first analysed. Then the compensation effects of gyros' stochastic errors brought by the rotation technique are discussed by comparing the error propagation characteristics in an INS and a RINS. In order to verify the theory, a large number of simulations are carried out. The simulation results show a good consistency with the derived formulae, which can indicate the correctness of the theory.

Fusing Kinect sensor and inertial sensors with multi-rate Kalman filter

Abstract: This paper presents a sensor fusion approach to fusing Microsoft Kinect sensor and the built-in inertial sensors in a mobile device. A multi-rate Kalman filter is designed and applied for fusing the low-sampling-rate (30Hz) uncertain positions sensed by the Kinect sensor and the high-sampling-rate (90Hz) accelerations measured by the inertial sensors. These sensors have complementary properties. The Kinect can be applied for skeleton tracking, which gives the joints' positions. Meanwhile, the built-in inertial sensors in the mobile device sense the hand motion and the acceleration can be estimated through inertial sensor fusion. Firstly, convert the acceleration estimated with inertial sensors from the body frame into the Kinect coordinate system. Experimental results show that the hand accelerations estimated with the Kinect sensor and the inertial sensors are comparable. Secondly, design and apply a multi-rate Kalman filter for sensor fusion. The sensor fusion helps improve the accuracy of the system state estimation including the position, the velocity and the acceleration. This is of great benefit for combining inertial sensors and the external position sensing device for indoor augmented reality (AR) and other location-aware sensing applications.

Heading confidence interval estimation

Abstract: An inertial measurement system is disclosed. The inertial measurement system has an accelerometer processing unit that generates a calibrated accelerometer data. The inertial measurement system further includes a magnetometer processing unit generates a calibrated magnetometer data, and a gyroscope processing unit generates a calibrated gyroscope data. Using the calibrated accelerometer data, the calibrated magnetometer data, and the calibrated gyroscope data, the inertial measurement system generates a heading angle error indicative of the accuracy of the heading angle error.

Development of a high-precision calibration method for inertial measurement unit

Abstract: MEMS (Micro-Electromechanical Systems) based IMU (Inertial Measurement Unit) including accelerometers and gyroscopes is widely used for various applications such as INS (Inertial Navigation System), pose estimation devices and others for many industries such as toy, medical, automotive and military industry But MEMS sensor chip originally has bias and sensitivity errors from manufacturing, and there is also axis misalignment when mounting a MEMS chip on IMU PCB layer These error factors cause inaccuracy measurement results and non-linear measurement characteristics of IMU In this paper, accelerometers and gyroscopes are mathematically modeled based on these error factors including bias, sensitivity, coning angle and azimuth angle Calibration procedures for accelerometers and gyroscopes are formulated using nonlinear Gauss-Newton regression logic The effectiveness of the proposed calibration procedures are proven by simulation and experiment using high accuracy 2-axis rotational gimbal motion system

Adaptive Kalman filter for MEMS-IMU based attitude estimation under external acceleration and parsimonious use of gyroscopes

Abstract: This paper considers rigid body attitude estimation from a small inertial/magnetic sensor module containing triaxial gyroscopes, accelerometers, and magnetometers. Precisely, two challenges are addressed. The first one concerns attitude estimation during various dynamic conditions, in which external acceleration occurs. Although external acceleration is one of the main source of loss of performance in attitude estimation methods, this problem has not been sufficiently addressed in the literature. An adaptive algorithm compensating external acceleration from the residual in the accelerometer is proposed. At each step, the covariance matrix associated with the external acceleration is estimated to adaptively tune the filter gain. The second challenge is focused on the energy consumption issue of gyroscopes for long-term battery life of Inertial Measurement Units. We study the way to reduce the gyro measurement acquisition while maintaining acceptable attitude estimation. Through numerical simulations, under external acceleration and parsimonious gyroscope's use, the efficiency of the proposed q-AKF is illustrated.

Design and implementation of sensor data fusion for an autonomous quadrotor

Abstract: This paper describes the design and integration of the instrumentation and sensor fusion that is used to allow the autonomous flight of a quadrotor. A comercial frame is used, a mathematical model for the quadrotor is developed and its parameters determined from the characterization of the unit. A 9 degrees of freedom Inertial Measurement Unit (IMU) equipped with a barometer is calibrated and added to the platform. Sensor fusion is done by two modified Extended Kalman Filters (EKF): one combining data provided by IMU and the other also including the information provided by GPS. A reliable estimation of the state variables is obtained. Three states representing systematic bias in the accelerometer measurements are also added to the EKF, which improves the inertial estimation of the position. A stable autonomous platform is achieved.

System and method for magnetometer calibration and compensation

Abstract: A system comprises an inertial measurement unit comprising one or more gyroscopes configured to measure angular velocity about a respective one of three independent axes and one or more accelerometers configured to measure specific force along a respective one of the three independent axes; a magnetometer configured to measure strength of a local magnetic field along each of the three independent axes; and a processing device coupled to the inertial measurement unit and the magnetometer; the processing device configured to compute kinematic state data for the system based on measurements received from the magnetometer and the inertial measurement unit. The processing device is further configured to calculate magnetometer measurement calibration parameters using a first technique when position data is unavailable and to calculate magnetometer measurement calibration parameters using a second technique when position data is available.

An adaptive complementary filter for inertial sensor based data fusion to track upper body motion

Abstract: Remote human activity monitoring is critical and essential in physiotherapy with respect to the skyrocketing healthcare expenditure and the fast aging population. One of frequently used method to monitor human activity is wearing inertial sensors since it is low-cost and accurate. However, the measurements of those sensors are able only to estimate the orientation and rotation angles with respect to actual movement angles, because of differences in the body's co-ordination system and the sensor's co-ordination system. There were numerous studies being conducted to improve the accuracy of estimation, though there is potential for further discussions on improving accuracy by replacing heavy algorithms to less complexity. This research is an attempt to propose an adaptive complementary filter for identifying human upper arm movements. Further, this article discusses a feasibility of upper arm rehabilitation using the proposed adaptive complementary filter and inertial measurement sensors. The proposed algorithm is tested with four healthy subjects wearing an inertial sensor against gold standard, which is the VICON system. It demonstrated root mean squared error of 8.77° for upper body limb orientation estimation when compared to gold standard VICON optical motion capture system.

Indoor positioning using ultra-wideband and inertial measurements

Abstract: In this thesis, we consider the problem of estimating position and orientation (6D pose) using inertial sensors (accelerometers and gyroscopes). Inertial sensors provide information about the change in position and orientation at high sampling rates. However, they suffer from integration drift and hence need to be supplemented with additional sensors. To combine information from the inertial sensors with information from other sensors we use probabilistic models, both for sensor fusion and for sensor calibration.Inertial sensors can be supplemented with magnetometers, which are typically used to provide heading information. This relies on the assumption that the measured magnetic field is equal to a constant local magnetic field and that the magnetometer is properly calibrated. However, the presence of metallic objects in the vicinity of the sensor will make the first assumption invalid. If the metallic object is rigidly attached to the sensor, the magnetometer can be calibrated for the presence of this magnetic disturbance. Afterwards, the measurements can be used for heading estimation as if the disturbance was not present. We present a practical magnetometer calibration algorithm that is experimentally shown to lead to improved heading estimates. An alternative approach is to exploit the presence of magnetic disturbances in indoor environments by using them as a source of position information. We show that in the vicinity of a magnetic coil it is possible to obtain accurate position estimates using inertial sensors, magnetometers and knowledge of the magnetic field induced by the coil.We also consider the problem of estimating a human body’s 6D pose. For this, multiple inertial sensors are placed on the body. Information from the inertial sensors is combined using a biomechanical model which represents the human body as consisting of connected body segments. We solve this problem using an optimization-based approach and show that accurate 6D pose estimates are obtained. These estimates accurately represent the relative position and orientation of the human body, i.e. the shape of the body is accurately represented but the absolute position can not be determined.To estimate absolute position of the body, we consider the problem of indoor positioning using time of arrival measurements from an ultra-wideband (uwb) system in combination with inertial measurements. Our algorithm uses a tightlycoupled sensor fusion approach and is shown to lead to accurate position and orientation estimates. To be able to obtain position information from the uwb measurements, it is imperative that accurate estimates of the receivers’ positions and clock offsets are known. Hence, we also present an easy-to-use algorithm to calibrate the uwb system. It is based on a maximum likelihood formulation and represents the uwb measurements assuming a heavy-tailed asymmetric noise distribution to account for measurement outliers.

Probabilistic modeling for positioning applications using inertial sensors

Abstract: In this thesis, we consider the problem of estimating position and orientation (6D pose) using inertial sensors (accelerometers and gyroscopes). Inertial sensors provide information about the chang ...

On-board inertial-assisted visual odometer on an embedded system

Abstract: In this paper, we propose a novel inertial-assisted visual odometry system intended for low-cost micro aerial vehicles (MAVs). The system sensor assembly consists of two downward facing cameras and a inertial measurement unit (IMU) with three-axis accelerometers/gyroscopes. Real-time implementation of the system is enabled by a low-cost embedded system via two important features: first, simple pixel-level algorithms are integrated in a low-end FPGA and accelerated via pipeline and combinational logic techniques; second, a novel outlier rejection algorithm is implemented which is based on probability-based predetermined operations rather than hypothesis testing iterations. We illustrate the performance of our system by hovering a MAV in a GPS-denied environment. Its feasibility and robustness is also illustrated in a complex outdoor environment.

Polynomial degree determination for temperature dependent error compensation of inertial sensors

Abstract: MEMS based Inertial Measurement Units, which include MEMS accelerometers and gyroscopes, have a wide range of applications due to their low cost, small size and low power consumption. IMU error should be reduced to the lowest level so as to minimize errors of navigation systems which use MEMS IMU. MEMS sensors have bias, scale factor as well as temperature change of these errors. In the error compensation algorithm, polynomials are used to compensate temperature dependent change of errors. The degree of polynomials, which are determined according to sensor characteristics, affect IMU performance. Therefore, optimum degree must be determined by various methods and studies. In this paper, temperature dependent errors of MEMS inertial sensors will be defined and how to determine degree of polynomial will be explained so as to compensate errors by the optimum way. Several simulation studies are supported with real sensor data.

Improving attitude estimation using inertial sensors for quadrotor control systems

Abstract: Attitude estimation for an aerial vehicle using the Kalman Filter - KF- with experimental validation is presented in this paper. The data fusion is made using simplified representations of the kinematics of the aerial vehicle and the accelerometer measurement model. The resulting algorithm is computationally efficient as it can be run at up to 500 Hz on a low-cost microcontroller. The observer is improved by choosing the appropriate covariance and noise matrices. Numerical and in-flight validation are carried out using an experimental platform and a quadrotor prototype. The experimental results are compared online with the measurements coming from a commercial IMU -Inertial Measurement Unit.

A method to calibrate installation orientation errors of inertial sensors for gait analysis

Abstract: Gait analysis by using wearable inertial sensors has gained tremendous achievements in recent years. A limitation of using inertial measurement unit (IMU) is that inertial signals heavily rely on the correctness of sensor installation. A slight installation orientation error could lead to inaccurate results for quantitative estimation of gait parameters. In this paper, a calibration method was proposed to deal with installation orientation errors of IMU, where the errors were considered as an IMU frame rotating around the standard coordinate system. The proposed method could generate a three-dimensional rotation matrix to calibrate gait signals. By using the calibrated signals, estimation of gait parameters such as calculation of stride length and detection of gait phase can be more accurate. An experiment was performed on our gait monitoring platform based on IMU, and the experimental results demonstrate the effectiveness of the proposed method.

Wearable Inertial Sensors and Their Applications

Abstract: Wearable inertial sensors have been developed extensively over the past several years. Inertial sensors, including accelerometers, gyroscopic sensors, and magnetic sensors, can be embedded in the body, such as the trunk, leg, arm, etc., for monitoring the motion associated with human activities. In this chapter, we examine the technological principles of several types of inertial sensors, and provide an assessment and evaluation of these sensors for patient rehabilitation in clinical practice.

Line transfer system for airplane

Abstract: A method is provided for establishing a physical reference inside an airplane representing the airplane's optimized line of flight based on the as-built orientation of aerodynamically significant features of the airplane. Values generated for aerodynamic pitch, roll and yaw representing the optimized line of flight are used to orient a tool reference surface outside the airplane. The orientation of the tool reference surface is recorded using an inertial reference unit placed on the tool reference surface. The tool reference surface and inertial reference unit are moved into the airplane where they are used to establish the physical reference on the airframe.

A case study on sensors and techniques for pedestrian inertial navigation

Abstract: The interest in location based services is growing in several applications. The literature exhibits a wide spectrum of technology to complement the well-knwon limitations of satellite based positioning systems in constrained environments such as indoors or urban canyons. This paper focuses on inertial sensors and systems to locate pedestrians indoor without infrastructure. The theoretical background of a recently developped belt-mounted inertial navigation system (INS) is carefully depicted here. The approach aims to facilitate the equipment and the mobility of the users while maintaining repeatable performance. Therefore, a comparison of the performance in realistic conditions is carried out between foot-mounted and belt-mounted techniques given an inertial measurement unit (IMU) commercialized by XSens. Then, this commercial IMU and an IMU based on ADXL345 and ITG3200 were compared, given the belt-mounted algorithm, in terms of positioning performance. The results, supported by dozens of experiments involving different participants, show that the belt-mounted technique is as efficient as the foot-mounted one since the average error in position is less than 2% of the travelled distance about 200m. Whereas their costs are very different, the commercial and the integrated IMU reach a similar accuracy. The belt-mounted device achieve repeatable and efficient pedestrian indoor positioning in real-time with low-cost inertial sensors.

Error modulation scheme analysis of dual-axis rotating strap-down inertial navigation system based on FOG

Abstract: In order to enhance the accuracy of strap-down inertial navigation system, we study the error models of each inertial sensor. Based on the error effects, the rotation modulation technique is analyzed. For the case of dual-axial rotation, the error equations are derived which are produced by bias, installation error, scale factor error and random drift of inertial sensors. This paper proposes simultaneous rotation and alternately rotation, analyzes two schemes modulation mechanism and gives two reasonable rotating axes angle velocities. The simulation result presents that both rotation schemes can effectively modulate system error, and compares the modulation performance of each scheme over every navigation error.