Showing papers on "Inertial reference unit published in 2015"

A Novel Kalman Filter for Human Motion Tracking With an Inertial-Based Dynamic Inclinometer

Abstract: Goal: Design and development of a linear Kalman filter to create an inertial-based inclinometer targeted to dynamic conditions of motion. Methods: The estimation of the body attitude (i.e., the inclination with respect to the vertical) was treated as a source separation problem to discriminate the gravity and the body acceleration from the specific force measured by a triaxial accelerometer. The sensor fusion between triaxial gyroscope and triaxial accelerometer data was performed using a linear Kalman filter. Wrist-worn inertial measurement unit data from ten participants were acquired while performing two dynamic tasks: 60-s sequence of seven manual activities and 90 s of walking at natural speed. Stereophotogrammetric data were used as a reference. A statistical analysis was performed to assess the significance of the accuracy improvement over state-of-the-art approaches. Results: The proposed method achieved, on an average, a root mean square attitude error of 3.6° and 1.8° in manual activities and locomotion tasks (respectively). The statistical analysis showed that, when compared to few competing methods, the proposed method improved the attitude estimation accuracy. Conclusion: A novel Kalman filter for inertial-based attitude estimation was presented in this study. A significant accuracy improvement was achieved over state-of-the-art approaches, due to a filter design that better matched the basic optimality assumptions of Kalman filtering. Significance: Human motion tracking is the main application field of the proposed method. Accurately discriminating the two components present in the triaxial accelerometer signal is well suited for studying both the rotational and the linear body kinematics.

Human Joint Angle Estimation with Inertial Sensors and Validation with A Robot Arm

Abstract: Traditionally, human movement has been captured primarily by motion capture systems. These systems are costly, require fixed cameras in a controlled environment, and suffer from occlusion. Recently, the availability of low-cost wearable inertial sensors containing accelerometers, gyroscopes, and magnetometers have provided an alternative means to overcome the limitations of motion capture systems. Wearable inertial sensors can be used anywhere, cannot be occluded, and are low cost. Several groups have described algorithms for tracking human joint angles. We previously described a novel approach based on a kinematic arm model and the Unscented Kalman Filter (UKF). Our proposed method used a minimal sensor configuration with one sensor on each segment. This paper reports significant improvements in both the algorithm and the assessment. The new model incorporates gyroscope and accelerometer random drift models, imposes physical constraints on the range of motion for each joint, and uses zero-velocity updates to mitigate the effect of sensor drift. A high-precision industrial robot arm precisely quantifies the performance of the tracker during slow, normal, and fast movements over continuous 15-min recording durations. The agreement between the estimated angles from our algorithm and the high-precision robot arm reference was excellent. On average, the tracker attained an RMS angle error of about $3^\circ$ for all six angles. The UKF performed slightly better than the more common Extended Kalman Filter

A Self-Calibration Method for Nonorthogonal Angles Between Gimbals of Rotational Inertial Navigation System

Abstract: Navigation accuracy of an inertial navigation system can be significantly enhanced by rotating inertial measurement unit with gimbals. Therefore, nonorthogonal angles of gimbals, which are coupled into the navigation error during rotation, should be calibrated and compensated effectively. In this paper, the relationship model of nonorthogonal angles and navigation error is established. Then, the calibration scheme and observation equation during gimbals rotation is proposed. Proved by a piecewise constant system method, all of the error parameters are observable and can be estimated by an extended Kalman filter. Experimental results show that compared with the traditional method, the proposed method can substantially reduce velocity error on static base. Moreover, the position accuracy of long-term navigation under moving base is also significantly increased.

Augmented reality computing with inertial sensors

Abstract: Example embodiments of the present disclosure provide techniques for receiving measurements from one or more inertial sensors (i.e. accelerometer and angular rate gyros) attached to a device with a camera or other environment capture capability. In one embodiment, the inertial measurements may be combined with pose estimates obtained from computer vision algorithms executing with real time camera images. Using such inertial measurements, a system may more quickly and efficiently obtain higher accuracy orientation estimates of the device with respect to an object known to be stationary in the environment.

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.

Navigation using inertial sensors [Tutorial]

Abstract: This tutorial provides an introduction to navigation using inertial sensors, explaining the underlying principles. Topics covered include accelerometer and gyroscope technology and their characteristics, strapdown inertial navigation, attitude determination, integration and alignment, zero updates, motion constraints, pedestrian dead reckoning using step detection, and fault detection.

Navigation Using Inertial Sensors

Abstract: This tutorial provides an introduction to navigation using inertial sensors, explaining the underlying principles. Topics covered include accelerometer and gyro technology and their characteristics, strapdown inertial navigation, attitude determination, integration and alignment, zero updates, motion constraints, pedestrian dead reckoning using step detection, and fault detection.

Hand Pose Estimation by Fusion of Inertial and Magnetic Sensing Aided by a Permanent Magnet

Abstract: Tracking human body motions using inertial sensors has become a well-accepted method in ambulatory applications since the subject is not confined to a lab-bounded volume. However, a major drawback is the inability to estimate relative body positions over time because inertial sensor information only allows position tracking through strapdown integration, but does not provide any information about relative positions. In addition, strapdown integration inherently results in drift of the estimated position over time. We propose a novel method in which a permanent magnet combined with 3-D magnetometers and 3-D inertial sensors are used to estimate the global trunk orientation and relative pose of the hand with respect to the trunk. An Extended Kalman Filter is presented to fuse estimates obtained from inertial sensors with magnetic updates such that the position and orientation between the human hand and trunk as well as the global trunk orientation can be estimated robustly. This has been demonstrated in multiple experiments in which various hand tasks were performed. The most complex task in which simultaneous movements of both trunk and hand were performed resulted in an average rms position difference with an optical reference system of $19.7\pm 2.2$ mm whereas the relative trunk-hand and global trunk orientation error was $2.3\pm 0.9$ and $8.6\pm 8.7$ deg respectively.

A Joint-Angle Estimation Method for Industrial Manipulators Using Inertial Sensors

Abstract: This paper focuses on the use of low-cost inertial sensors in industrial manipulators. In particular, an easy-to-install joint-angle estimation method is proposed. This scheme can work independently from the primary encoder-based sensor, for the creation of a fault-detection system. It can also be the primary joint-angle sensor in low-cost robotic manipulators. A robot with six degrees of freedom has been used, with three inertial measurement units set on its arms; one for each pair of joints. Three cascaded extended Kalman filters have been used to estimate the joint angles by the fusion of the outputs of triaxial gyroscopes and accelerometers. The results of three experimental tests are reported in order to show the performance and limitations of this approach, in comparison with the simple gyroscope measurements integration.

Fusing Inertial Sensor Data in an Extended Kalman Filter for 3D Camera Tracking

Abstract: In a setup where camera measurements are used to estimate 3D egomotion in an extended Kalman filter (EKF) framework, it is well-known that inertial sensors (i.e., accelerometers and gyroscopes) are especially useful when the camera undergoes fast motion. Inertial sensor data can be fused at the EKF with the camera measurements in either the correction stage (as measurement inputs) or the prediction stage (as control inputs). In general, only one type of inertial sensor is employed in the EKF in the literature, or when both are employed they are both fused in the same stage. In this paper, we provide an extensive performance comparison of every possible combination of fusing accelerometer and gyroscope data as control or measurement inputs using the same data set collected at different motion speeds. In particular, we compare the performances of different approaches based on 3D pose errors, in addition to camera reprojection errors commonly found in the literature, which provides further insight into the strengths and weaknesses of different approaches. We show using both simulated and real data that it is always better to fuse both sensors in the measurement stage and that in particular, accelerometer helps more with the 3D position tracking accuracy, whereas gyroscope helps more with the 3D orientation tracking accuracy. We also propose a simulated data generation method, which is beneficial for the design and validation of tracking algorithms involving both camera and inertial measurement unit measurements in general.

Calibration of an IMU-Camera Cluster Using Planar Mirror Reflection and Its Observability Analysis

Abstract: This paper describes a novel and a low-cost calibration approach to estimate the relative transformation between an inertial measurement unit (IMU) and a camera, which are rigidly mounted together. The calibration is performed by fusing the measurements from the IMU-camera rig moving in front of a planar mirror. To construct the visual observations, we select a set of key features (KFs) attached to the visual inertial rig where the 3-D positions of the KFs are unknown. During calibration, the system is navigating in front of the planar mirror, while the vision sensor observes the reflections of the KFs in the mirror, and the inertial sensor measures the system's linear accelerations and rotational velocities over time. Our first contribution in this paper is studying the observability properties of IMU-camera calibration parameters. For this visual inertial calibration problem, we derive its time-varying nonlinear state-space model and study its observability properties using the Lie derivative rank condition test. We show that the calibration parameters and the 3-D position of the KFs are observable. As our second contribution, we propose an approach for estimating the calibration parameters along with the 3-D position of the KFs and the dynamics of the analyzed system. The estimation problem is then solved in the unscented Kalman filter framework. We illustrate the findings of our theoretical analysis using both simulations and experiments. The achieved performance indicates that our proposed method can conveniently be used in consumer products like visual inertial-based applications in smartphones for localization, 3-D reconstruction, and surveillance applications.

Realtime assessment of foot orientation by Accelerometers and Gyroscopes

Abstract: Abstract Foot orientation can be assessed in realtime by means of a foot-mounted inertial sensor. We consider a method that uses only accelerometer and gyroscope readings to calculate the foot pitch and roll angle, i.e. the foot orientation angle in the sagittal and frontal plane, respectively. Since magnetometers are avoided completely, the method can be used indoors as well as in the proximity of ferromagnetic material and magnetic disturbances. Furthermore, we allow for almost arbitrary mounting orientation in the sense that we only assume one of the local IMU coordinate axes to lie in the sagittal plane of the foot. The method is validated with respect to a conventional optical motion capture system in trials with transfemoral amputees walking with shoes and healthy subjects walking barefoot, both at different velocities. Root mean square deviations of less than 4° are found in all scenarios, while values near 2° are found in slow shoe walking. This demonstrates that the proposed method is suitable for realtime application such as the control of FES-based gait neuroprostheses and active orthoses.

A Kalman Filter Based Attitude Heading Reference System Using a Low Cost Inertial Measurement Unit

Abstract: This paper describes the development of a sensor fusion algorithm-based Kalman filter architecture, for an Attitude Heading Reference System (AHRS) based on a low cost Inertial Measurement Unit (IMU). The system is verified with real time manual flight testing as well as autonomous waypoint flight testing. A low cost IMU uses Micro-ElectricalMechanical System(MEMS) technology using inexpensive, compact, and low grade sensors. The use of low cost IMUs is primarily targeted towards Unmanned Aerial Vehicle (UAV) applications due to the requirements for small package size, low energy consumption, and high sampling rate. The high dynamics nature of smaller airframes, coupled with the typical vibration induced noise of UAVs require an efficient, reliable, and robust Attitude Heading Reference System (AHRS) for vehicle control. To eliminate the singularities at ±90◦ and to maximize the computational efficiency, quaternions are used for state attitude representation.

An Approach for Observing and Modeling Errors in MEMS-Based Inertial Sensors Under Vehicle Dynamic

Abstract: This paper studies the error behavior of low-cost inertial sensors in dynamic conditions. After proposing a method for error observations per sensor (i.e., gyroscope or accelerometer) and axes, their properties are estimated via the methodology of generalized method of wavelet moments. The developed model parameters are compared with those obtained under static conditions. Then, an attempt is presented to link the parameters of the established model to the dynamic of the vehicle. It is found that a linear relation explains a large portion of the exhibited variability. These findings suggest that the static methods employed for the calibration of inertial sensors could be improved when exploiting such a relationship.

Coarse alignment of a shipborne strapdown inertial navigation system using star sensor

Abstract: This study presents a novel estimation method for fast initial coarse alignment of a shipborne strapdown inertial navigation system. Unlike several current techniques, the alignment accuracy of the presented estimation method is not reduced by the shipborne disturbances causing by wave action (when moored) and the inertial measurement unit output constant error. The coarse alignment method is based on the acceleration sensed by accelerometers and the attitude matrix respect to the inertial frame sensed by star sensor. The specific force along the inertial frame is derived from the acceleration sensed by accelerometers, and the information characteristics of the specific force along the inertial frame are discussed. The analysed results show that the constant and periodical information is included in the transformation specific force along the inertial frame. Therefore Butterworth low-pass filter is used to extract the constant information to calculate the attitude of the vehicle. The effectiveness of this approach was demonstrates by simulation and experimental study. The results showed this coarse alignment method can estimate attitude information, and the attitude errors of the alignment result are small angles. Therefore the alignment accuracy meets the demands of fine alignment.

Attitude and gyro bias estimation by the rotation of an inertial measurement unit

Abstract: In navigation applications, the presence of an unknown bias in the measurement of rate gyros is a key performance-limiting factor. In order to estimate the gyro bias and improve the accuracy of attitude measurement, we proposed a new method which uses the rotation of an inertial measurement unit, which is independent from rigid body motion. By actively changing the orientation of the inertial measurement unit (IMU), the proposed method generates sufficient relations between the gyro bias and tilt angle (roll and pitch) error via ridge body dynamics, and the gyro bias, including the bias that causes the heading error, can be estimated and compensated. The rotation inertial measurement unit method makes the gravity vector measured from the IMU continuously change in a body-fixed frame. By theoretically analyzing the mathematic model, the convergence of the attitude and gyro bias to the true values is proven. The proposed method provides a good attitude estimation using only measurements from an IMU, when other sensors such as magnetometers and GPS are unreliable. The performance of the proposed method is illustrated under realistic robotic motions and the results demonstrate an improvement in the accuracy of the attitude estimation.

Milli-HRG inertial sensor assembly – a reality

Abstract: The Hemispherical Resonator Gyro (HRG) has proven itself to be an ultra-reliable technology for space application with over 30 million operation hours and 100% mission success. Northrop Grumman Advanced Navigation Systems is developing a small size and lightweight terrestrial inertial sensor assembly (ISA) based on the HRG approach. This ISA will be the core of future inertial measurement unit (IMU) and inertial navigation system (INS) product lines.

Gps-aided inertial navigation method and system

Abstract: A GPS-aided inertial navigation method includes providing multiple sensors including multiple inertial measurement units (IMUs) and at least one global positioning system receivers and antennas (GPSs) and computer with embedded navigation software. The computer interfaces with all IMUs and all GPS receivers; running, in parallel, in multiple standard inertial navigation (IN) schemes; computes the mean of all the INSs to obtain a fused IN solution for the IMUs' mean location; computes the mean of all the GPS solutions to obtain a fused GPS solution for the GPS antennas' mean location; applies a lever-arm correction, with the vector from the mean IMU location to the ‘mean antenna’ location, to the fused GPS solution; feeds the fused IN solution and the lever-arm corrected fused GPS solution to a single navigation filter, as if there were a single IMU and a single GPS; and runs an IMU/IN/GPS correction module.

Drift reduction in IMU-only pedestrian navigation system in unstructured environment

Abstract: This paper aims at developing algorithmic approach to reduce the error drift in navigation information using only single motion sensor ie inertial measurement unit (IMU) in an unstructured environment The target application is pedestrian navigation system (PNS), but these algorithms are general for other applications too involving autonomous systems A MEMS IMU is mounted on the foot of the agent in order to get benefit of known walking patterns of the wearer Algorithms are derived based on motion constraints of human walking and applied to reduce the inherent error drift in inertial navigation systems (INS) Experiments are conducted in indoor/outdoor unstructured environment to validate this algorithmic approach and shown that without using any other sensors and any other pre-conditions on the environment; we can reduce navigation errors significantly only by taking into account the motion constraints of human walking

Development of attitude and heading reference systems

Abstract: The development of an attitude and heading reference system (AHRS) using sensor fusion is presented in this paper. The system consists of a nine degree of freedom inertial measurement unit (IMU) and a digital signal processor (DSP) module. The IMU comprises with triaxial gyroscopes, triaxial accelerometers, and triaxial magnetometers. By using the complementary filter, gain-scheduled complementary filter, and Kalman filter, the sensor fusion algorithms are implemented for the AHRS system. To verify the performance of the developed system, an experimental testbed of three-axis rotating platform is designed and constructed for experimental studies. It is shown that the robust performance can be obtained even though the system is in a dynamic state in the presence of a strong acceleration.

Estimating the orientation of a rigid body moving in space using inertial sensors

Abstract: This paper presents a novel method of estimating the orientation of a rigid body moving in space from inertial sensors, by discerning the gravitational and inertial components of the accelerations. In this method, both a rigid-body kinematics model and a stochastic model of the human-hand motion are formulated and combined in a nonlinear state-space system. The state equation represents the rigid body kinematics and stochastic model, and the output equation represents the inertial sensor measurements. It is necessary to mention that, since the output equation is a nonlinear function of the state, the extended Kalman filter (EKF) is applied. The absolute value of the error from the proposed method is shown to be less than 5 deg in simulation and in experiments. It is apparently stable, unlike the time-integration of gyroscope measurements, which is subjected to drift, and remains accurate under large accelerations, unlike the tilt-sensor method.

System based on inertial sensors for behavioral monitoring of wildlife

Abstract: Sensors Network is an integration of multiples sensors in a system to collect information about different environment variables. Monitoring systems allow us to determine the current state, to know its behavior and sometimes to predict what it is going to happen. This work presents a monitoring system for semi-wild animals that get their actions using an IMU (inertial measure unit) and a sensor fusion algorithm. Based on an ARM-CortexM4 microcontroller this system sends data using ZigBee technology of different sensor axis in two different operations modes: RAW (logging all information into a SD card) or RT (real-time operation). The sensor fusion algorithm improves both the precision and noise interferences.

Calibration of laser inertial navigator with dual-axis rotation

Abstract: The calibration of inertial measurement units (IMU) is required to determine the coefficients in the input/output models of inertial sensors. High accuracy turntables and information on the north direction and the local level are usually required in traditional calibration methods. But for a laser inertial navigator with dual-axis rotation, in which the IMU is usually compactly designed, it is usually not convenient to remove the IMU from the navigator to high precision turntable for calibration. To improve the affordability, a simple calibration method is proposed using the dual-axis rotation function of the navigator itself. The method relaxes the orthogonality requirement of the rotation axes and eliminates the need of north information. Rough knowledge of the local plumb-line direction is enough to determine the signs of accelerometer calibration parameters. No re-installation of the IMU is required during the calibration and thus it makes automatic calibration easy to implement. Simulations show that the proposed method is feasible. The proposed method is also tested on an experimental laser inertial navigator with dual-axis rotation, and the navigation results show that the calibration accuracy meets the requirements of high performance inertial navigations.

Magnetic Field Swimmer Positioning

Abstract: Several projects have tracked the movement of swimmers in pools using body worn inertial measurement units. In swimming, inertial sensing is subject to large amounts of drift and accumulated error which can only be corrected for after a complete length has been swum. In this article, we present a new method for tracking swimmers by detecting variations in the magnetic field caused by the structure of pools. This method is complementary to inertial positioning, as it allows the direct extraction of position without requiring post-processing, and unlike inertial sensing which loses accuracy over time, magnetic field tracking becomes increasingly accurate towards the end of a length.

Inertial sensing for gait analysis and the scope for sensor fusion

Abstract: Conventional gait analysis systems make use of cameras and are therefore bounded to a restricted area. In this work, we focus on developing a low-cost, wearable gait analysis system based on inertial sensors, as a suitable ambulatory alternative to be used in unconstrained locations. The developed system is tailored to study lower body motion in terms of gait parameters and joint kinematics. The main focus is in using the inertial sensors data, e.g. joint acceleration and quaternion rotation readings, for the reconstruction of visually appealing motion sequences from low-dimensional sensor input. This requires inertial sensor data to be mapped and fused with optical motion capture data. For this purpose, we propose to use a hierarchical skeleton model obtained from an optical motion capture database.

Low latency methodologies for a headset-mounted camera on virtual reality displays

Abstract: Systems and methods for camera and inertial sensor integration are described. The systems and methods may include receiving inertial data from one or more inertial sensors; processing the inertial data with an inertial sensor algorithm to produce an inertial sensor position and orientation; receiving camera data from one or more cameras; processing the camera data and the inertial sensor position with a camera sensor algorithm to produce a camera position and orientation; receiving the inertial sensor position and the camera position in a Kalman filter to determine position or orientation of a user wearing a virtual reality headset; and providing the user's position or orientation to the virtual reality headset. An apparatus that incorporates these systems and methods is also set forth.

On attitude observers and inertial navigation for reference system fault detection and isolation in dynamic positioning

Abstract: Marine craft employing a dynamic positioning system rely on measurements from position reference systems and gyrocompasses, and faults in these systems and sensors pose a serious risk of vessel drive-offs, that may in turn have dire consequences. An inertial measurement unit may provide independent position and orientation information, provided that the attitude estimates of the observer in use are accurate enough. In this paper we compare three nonlinear strapdown inertial-measurement-unit-based attitude estimators aided by global navigation satellite systems, and put them to test in a simulation of two fault detection and isolation scenarios. The comparison finds that all attitude estimators under test have the potential for use in a fault detection and isolation scheme.