Showing papers on "Inertial reference unit published in 2012"

Calibration of a magnetometer in combination with inertial sensors

Abstract: Measurements from magnetometers and inertial sensors (accelerometers and gyroscopes) can be combined to give 3D orientation estimates. In order to obtain accurate orientation estimates it is imperative that the magnetometer and inertial sensor axes are aligned and that the magnetometer is properly calibrated for both sensor errors as well as presence of magnetic distortions. In this work we derive an easy-to-use calibration algorithm that can be used to calibrate a combination of a magnetometer and inertial sensors. The algorithm compensates for any static magnetic distortions created by the sensor platform, magnetometer sensor errors and determines the alignment between the magnetometer and the inertial sensor axes. The resulting calibration procedure does not require any additional hardware. We make use of probabilistic models and obtain the calibration algorithm as the solution to a maximum likelihood problem. The efficacy of the proposed algorithm is illustrated using experimental data collected from a sensor unit placed in a magnetically disturbed environment onboard a jet aircraft.

Racket sport inertial sensor motion tracking and analysis

Abstract: A system for a racket comprises an inertial sensor for coupling to the racket, a processor connected to the inertial sensor, and a memory device connected to the processor. The inertial sensor includes an accelerometer array with three degrees of freedom in acceleration and a gyro array with three degrees of freedom in rotation. The processor is configured to generate stroke profiles describing acceleration and rotation of the racket based on signals from the accelerometer and the gyro arrays, and the memory device is configured to store the stroke profiles.

Detection of (In)activity Periods in Human Body Motion Using Inertial Sensors: A Comparative Study

Abstract: Determination of (in)activity periods when monitoring human body motion is a mandatory preprocessing step in all human inertial navigation and position analysis applications. Distinction of (in)activity needs to be established in order to allow the system to recompute the calibration parameters of the inertial sensors as well as the Zero Velocity Updates (ZUPT) of inertial navigation. The periodical recomputation of these parameters allows the application to maintain a constant degree of precision. This work presents a comparative study among different well known inertial magnitude-based detectors and proposes a new approach by applying spectrum-based detectors and memory-based detectors. A robust statistical comparison is carried out by the use of an accelerometer and angular rate signal synthesizer that mimics the output of accelerometers and gyroscopes when subjects are performing basic activities of daily life. Theoretical results are verified by testing the algorithms over signals gathered using an Inertial Measurement Unit (IMU). Detection accuracy rates of up to 97% are achieved.

A particle filter approach to indoor navigation using a foot mounted inertial navigation system and heuristic heading information

Abstract: Foot mounted inertial navigation is an effective method for obtaining high quality pedestrian navigation solutions from MEMS sensors. Zero-Velocity information from stationary periods in the step-cycle can be used to regularly correct position drift and update estimates of the inertial sensor biases, hence dramatically improving the navigation solution.

Design and implementation of IMU-based human arm motion capture system

Abstract: This paper presents the design of a generic inertial measurement unit (IMU) module for motion capture system. The module consists of 32-bit microcontroller with gyroscope, accelerometer, and magnetometer to provide orientation estimation of human limbs. An orientation estimation algorithm that compensates magnetic distortion is implemented. Our main contribution is on the sensor network which is low cost but high speed. We developed a serial-chain network for sensors interconnection in which UART communication is used. We also consider frame alignment issue in developing human arm motion capture to improve tracking accuracy. A simple frame calibration method is implemented and tested.

Gait Analysis Using Floor Markers and Inertial Sensors

Abstract: In this paper, a gait analysis system which estimates step length and foot angles is proposed. A measurement unit, which consists of a camera and inertial sensors, is installed on a shoe. When the foot touches the floor, markers are recognized by the camera to obtain the current position and attitude. A simple planar marker with 4,096 different codes is used. These markers printed on paper are placed on the floor. When the foot is moving off the floor, the position and attitude are estimated using an inertial navigation algorithm. For accurate estimation, a smoother is proposed, where vision information and inertial sensor data are combined. Through experiments, it is shown that the proposed system can both track foot motion and estimate step length.

UAV position and attitude estimation using IMU, GNSS and camera

Abstract: The aim of this paper is to present a method for integration of measurements provided by inertial sensors (gyroscopes and accelerometers), GPS and a video system in order to estimate position and attitude of an UAV (Unmanned Aerial Vehicle). Inertial sensors are widely used for aircraft navigation because they represent a low cost and compact solution, but their measurements suffer of several errors which cause a rapid divergence of position and attitude estimates. To avoid divergence inertial sensors are usually coupled with other systems as for example GNSS (Global Navigation Satellite System). In this paper it is examined the possibility to couple the inertial sensors also with a camera. A camera is generally installed on-board UAVs for surveillance purposes, it presents several advantages with respect to GNSS as for example great accuracy and higher data rate. Moreover, it can be used in urban area or, more in general, where multipath effects can forbid the application of GNSS. A camera, coupled with a video processing system, can provide attitude and position (up to a scale factor), but it has lower data rate than inertial sensors and its measurements have latencies which can prejudice the performances and the effectiveness of the flight control system. The integration of inertial sensors with a camera allows exploiting the better features of both the systems, providing better performances in position and attitude estimation.

Pitcher training apparatus and method using a ball with an embedded inertial measurement unit

Abstract: A measurement system having a miniature, wireless inertial measurement unit (IMU) disposed within or on a moving object, such as a ball or other member, to calculate the kinematics of the moving object.

Design and Implementation of a Nine-Axis Inertial Measurement Unit

Abstract: A nine-axis inertial measurement unit (IMU) that utilizes three-axis angular velocity measurements from rate gyroscopes and six-axis linear acceleration measurements from three two-axis accelerometers is reported. This system can derive linear acceleration, angular acceleration, and angular velocity via simple memoryless matrix operations, and eliminates the requirement for accelerometer installation at the center of mass as in the traditional IMU. An optimal configuration of the system is proposed based on the analysis of rigid body dynamics and matrix theory. In this configuration, the computed angular acceleration is free of the gravity effect as well. Analyses of sensor position and orientation errors are reported. Experimental validation was executed to evaluate the performance of the system.

Heading drift mitigation for low-cost inertial pedestrian navigation

Abstract: The concept of autonomous pedestrian navigation is often adopted for indoor pedestrian navigation. For outdoors, a Global Positioning System (GPS) is often used for navigation by utilizing GPS signals for position computation but indoors, its signals are often unavailable. Therefore, autonomous pedestrian navigation for indoors can be realized with the use of independent sensors, such as low-cost inertial sensors, and these sensors are often known as Inertial Measurement Unit (IMU) where they do not rely on the reception of external information such as GPS signals. Using these sensors, a relative positioning concept from initialized position and attitude is used for navigation. The sensors sense the change in velocity and after integration, it is added to the previous position to obtain the current position. Such low-cost systems, however, are prone to errors that can result in a large position drift. This problem can be minimized by mounting the sensors on the pedestrian’s foot. During walking, the foot is briefly stationary while it is on the ground, sometimes called the zero-velocity period. If a non-zero velocity is then measured by the inertial sensors during this period, it is considered as an error and thus can be corrected. These repeated corrections to the inertial sensor’s velocity measurements can, therefore, be used to control the error growth and minimize the position drift. Nonetheless, it is still inadequate, mainly due to the remaining errors on the inertial sensor’s heading when the velocity corrections are used alone. Apart from the initialization issue, therefore, the heading drift problem still remains in such low-cost systems. In this research, two novel methods are developed and investigated to mitigate the heading drift problem when used with the velocity updates. The first method is termed Cardinal Heading Aided Inertial Navigation (CHAIN), where an algorithm is developed to use building ‘heading’ to aid the heading measurement in the Kalman Filter. The second method is termed the Rotated IMU (RIMU), where the foot-mounted inertial sensor is rotated about a single axis to increase the observability of the sensor’s heading. For the CHAIN, the method proposed has been investigated with real field trials using the low-cost Microstrain 3DM-GX3-25 inertial sensor. It shows a clear improvement in mitigating the heading drift error. It offers significant improvement in navigation accuracy for a long period, allowing autonomous pedestrian navigation for as long as 40 minutes with below 5 meters position error between start and end position. It does not require any extra heading sensors, such as a magnetometer or visual sensors such as a camera nor an extensive position or map database, and thus offers a cost-effective solution. Furthermore, its simplicity makes it feasible for it to be implemented in real-time, as very little computing capability is needed. For the RIMU, the method was tested with Nottingham Geospatial Institute (NGI) inertial data simulation software. Field trials were also undertaken using the same low-cost inertial sensor, mounted on a rotated platform prototype. This method improves the observability of the inertial sensor’s errors, resulting also in a decrease in the heading drift error at the expense of requiring extra components.

Measurement-While-Drilling Instrument Based on Predigested Inertial Measurement Unit

Abstract: This paper developed a measurement-while-drilling instrument based on predigested inertial measurement unit (PIMU) and presented a PIMU design with a rotational modulation device, thus improving precision of inertial sensors. Then, this paper proposed an integral surveying method based on inertial navigation system and dead reckoning, measuring motion parameters characterized by long-term high accuracy. The semi-physical simulation was conducted under the laboratory conditions. The results indicate that, the suggested methodology dramatically improves measuring accuracy of attitude angles (azimuth, pitch, and roll) and position.

Multi-axis atomic inertial sensor system

Abstract: An inertial sensing system comprises a first multi-axis atomic inertial sensor, a second multi-axis atomic inertial sensor, and an optical multiplexer optically coupled to the first and second multi-axis atomic inertial sensors The optical multiplexer is configured to sequentially direct light along different axes of the first and second multi-axis atomic inertial sensors A plurality of micro-electrical-mechanical systems (MEMS) inertial sensors is in operative communication with the first and second multi-axis atomic inertial sensors Output signals from the first and second multi-axis atomic inertial sensors aid in correcting errors produced by the MEMS inertial sensors by sequentially updating output signals from the MEMS inertial sensors

Investigating arm symmetry in swimming using inertial sensors

Abstract: This research used an sacrum mounted self developed inertial sensor, which provides triaxial accelerometers and gyroscopes, RF capability and 2GB on-board memory in a waterproof casing to investigate the arm symmetry in freestyle swimming. The recorded acceleration data was filtered using a high-pass Hamming windowed FIR filter to remove the sensor orientation from the wanted signal. The signal was then analyzed using a zero-crossing detection algorithm to find the individual stroke rates (SR) and the differences between left and right arm stroke durations (asymmetry).

Estimation of conventional inertial sensor errors with atomic inertial sensor

Abstract: Embodiments described herein provide for a method for obtaining an inertial measurement. The method includes obtaining multiple contiguous high sample rate readings during a time period from a conventional inertial sensor. Non-contiguous low sample rate reading of accumulated motion are also obtained over the time period from an atomic inertial sensor. One or more observable errors are estimated for the conventional inertial sensor based on comparing the low sample rate reading to the multiple high sample rate readings. A compensated hybrid reading is determined by compensating the high sample rate readings for the one or more observable errors based on the estimating of the one or more observable errors.

Feasibility Study and Performance Analysis of a Gyroless Orientation Tracker

Abstract: Inertial orientation tracking systems commonly use three types of sensors: accelerometers, magnetometers, and gyroscopes. The angular rate signal is used to obtain a dead reckoning estimate, whereas the gravitational and local magnetic field measures allow us to apply a correction and to obtain a drift-free result. Considering the present market of inertial MEMS sensors, the current consumption of gyroscopes represents a major part of the power budget of wireless inertial sensor nodes, which should be minimized given the mobility of the application. This paper introduces an orientation tracking algorithm, based on an unscented Kalman filter, that does not require angular rate data for tracking human movements up to 450 °/s , which is a reasonable value for many applications. Since accelerometers measure other accelerations beside gravity and magnetometers are prone to magnetic disturbances, adaptive techniques are applied in order to reduce the influence on the estimations. The performance of the system is quantitatively analyzed and compared to an estimator that includes angular rate information.

Fiber optic gyros in a high-performance, high-reliability inertial reference unit for commercial satellites

Abstract: Honeywell Space Systems Division has successfully completed development of the Interferometric Fiber Optic Gyroscopes (IFOG) for a next-generation Inertial Reference Unit (IRU) to point commercial satellites. The design, build, and test phase of an engineering-model of this IRU, nicknamed Spirit, completed in 2011. The Spirit IRU complements the existing portfolio of Honeywell navigation products, such as the Miniature Inertial Measurement Unit (MIMU), by leveraging the long life and excellent performance inherent in FOG technology. This paper provides an overview of the Spirit system along with initial results from gyro performance and life testing.

Positioning apparatus and positioning method

Abstract: A positioning apparatus includes: a unit that calculates an inertial navigation positioning result by performing position calculation using inertial sensor data and stores the inertial navigation positioning result in a storage unit with time information being added to the inertial navigation positioning result; a unit that calculates a GPS positioning result by using GPS positioning data; a unit that performs a coupling process for the GPS positioning result and the inertial navigation positioning result, which is stored in the storage unit, having the same time information as time when the GPS positioning data is acquired; a unit that corrects the inertial navigation positioning result stored in the storage unit based on information of a position error, an attitude error, a velocity error, and a bias error of the inertial sensor that are acquired through the coupling process.

A wireless micro inertial measurement unit (IMU)

Abstract: In this paper, we present a wireless micro inertial measurement unit (IMU) with the smallest volume and weight requirements available at the moment. With a size of 18mm × 16mm × 4 mm, this IMU provides full control over the data of a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer. It meets the design prerequisites of a space-saving design and eliminates the need of a hard-wired data communication while still being comparable to state of the art commercially available MEMS IMUs. A CC430 microcontroller sends the collected raw data to a base station wirelessly with a maximum sensor sample rate of 640 samples per second. Thereby, the IMU performance is optimized by moving data post processing to the base station. This development offers important features in embedded microsystem applications with their significant size and weight requirements.

Systems and methods for compensating nonlinearities in a navigational model

Abstract: Systems and methods are provided for compensating nonlinearities in a navigational model. In one embodiment, a system comprises an inertial measurement unit configured to measure inertial motion of a vehicle and an aiding source configured to provide observational measurements of vehicle motion. Further, the system comprises a navigation computer coupled to the inertial measurement unit and the aiding source, wherein the navigation computer is configured to calculate a predicted state and an error covariance data based on the measured inertial motion received from the inertial measurement unit and the observational measurements from the aiding source and calculate variance increments based on attitude uncertainty in the predicted state. Also, the navigation computer is configured to add the variance increments into a process noise covariance matrix for the predicted state and calculate an update for the vehicle motion based on the predicted state, the error covariance data, and the observational measurements.

Insect inertial measurement units: gyroscopic sensing of body rotation

Abstract: All flying insects require both visual and mechanosensory information to effectively control flight in complex environments. Sometimes, however, visual systems may not have sufficiently short response times to accommodate for rapid perturbations in the flight path. Thus insects also rely on fast and precise mechanosensory systems as part of their flight control mechanism. We review a subclass of mechanosensory systems that serve as gyroscopic organs — otherwise known as inertial measurement units in engineering systems. We review current neurophysiological and behavioral data for two putative biological gyroscopes: cranefly halteres and the antennae of hawkmoths. In addition we present data that further clarifies the gyroscopic function of moth antennae, showing that they directly measure pitching motions. We also introduce new data on mechanosensory systems in the wings of moths and suggest that these too could provide critical inertial sensory information to flying animals.

Inertial Measurement Unit Calibration Procedure for a Redundant Tetrahedral Gyro Configuration with Wavelet Denoising

Abstract: The aim of this paper was to present a calibration procedure applied to an inertial measurement unit composed of a triad of accelerometers and four gyros in a tetrad configuration. The procedure has taken into account a technique based on least-square methods and wavelet denoising to perform the best estimate of the sensor axis misalignments. The wavelet analysis takes plance in order to remove undesirable high frequency components via multi-resolution signal decomposition analysis applied gyro signals. Equations for the least-square methods and wavelets analysis are presented, and the procedure is experimentally verified.

Systems and methods for characterizing turbulence regions

Abstract: Systems and methods for characterizing regions of turbulence are provided. In one implementation, a method includes: measuring turbulence with an inertial reference unit on an aircraft to acquire a turbulence measurement; recording a position of the aircraft associated with the turbulence measurement and the turbulence measurement on at least one memory device; processing the turbulence measurement on a processing unit to determine a turbulence intensity setting; determining a turbulence region for the recorded position; associating the turbulence region with the turbulence intensity setting; and transmitting the turbulence intensity setting and the associated turbulence region.

Method of calibrating an inertial assembly comprising a dynamic phase between two static phases

Abstract: A method of calibrating an inertial unit is provided. During a first static stage, in which the inertial unit is in a first orientation, measurements are taken by means of the accelerometers and the inertial rotation sensors. During a dynamic stage, the orientation of the inertial unit is changed, at least in part in azimuth, from the first orientation towards a second orientation, while taking measurements by means of the inertial rotation sensors. During a second static stage, in which the inertial unit is in the second position, measurements are taken by means of the accelerometers and of the inertial rotation sensors. For each static stage, a direction, an amplitude, and a mean speed of rotation for apparent gravity in an inertial frame of reference is estimated, variation is calculated in orientation between the static stages, and the accelerometer biases is deduced therefrom.

A fast alignment algorithm based on inertial frame for marine SINS

Abstract: A fast alignment algorithm based on inertial frame is proposed in this paper. Firstly, a third-order leveling loop is used for fine leveling alignment to establish a level coordinate frame. Secondly, the property that the projection of the gravitational vector in level coordinate frame is unrelated to heading angle is used to calculate the projection of the gravitational vector in body inertial coordinate frame. Thirdly, the projection of the gravitational vector is smoothed through weighted average algorithm, so that the fast alignment based on inertial frame is completed. Finally, the simulation and mooring test validate the speedability and accuracy of this fast alignment algorithm. The results show that the fast initial alignment algorithm can achieve the medium accuracy in 6 minutes. It fulfills the accuracy requirement for the medium accuracy marine SINS.

Design and fabrication of a vortex inertial sensor consisting of 3-DOF gyroscope and 3-DOF accelerometer

Abstract: This paper reports a novel vortex multi-axis inertial sensor, which can detect three components of angular rate and three components of linear acceleration simultaneously. It uses a vortex gas flow instead of the traditional linear gas flow as the inertial mass to detect the angular rate and linear acceleration. In the implementation, the vortex was formed by jetting the gas into a round chamber through two opposing nozzle orifices in the opposite direction. And the multi-axis detection was realized by a proper configuration of thermistors. The test results show that both the gyroscopes and accelerometers can reach a medium accuracy, which proves the feasibility of the device.

Gyroscope offset estimation using panoramic vision-based attitude estimation and Extended Kalman Filter

Abstract: Inertial sensors suffer from different types of errors that cause drift in the attitude measurement. Gyroscopes are subject to thermal drift that will cause the attitude to drift over time due to the integration of sensor error by the attitude algorithm. The gyroscope drift, named as offset, can be estimated using Extended Kalman Filter (EKF). In strapdown systems, the direction of gravity measured by accelerometers is used to correct the drift. However the tilt measured by the accelerometers is corrupted by the vehicle dynamics when turning or accelerating. Vision systems are not vulnerable to the vibration and can provide a good attitude reference. This paper investigates the attitude and the dynamic gyroscope offset estimation using an quaternion-based EKF using the attitude reference from a panoramic Ultra Violet (UV) vision system and an inertial based Attitude Heading Reference System (AHRS). The results of the proposed methods are validated against an AHRS.

Personal navigation method based on foot-mounted MEMS inertial/magnetic measurement unit

Abstract: In order to relieve the dependence of personal navigation on global navigation satellite system(GNSS),a personal navigation method was studied based on foot-mounted MEMS inertial/ magnetic measurement unit.Navigation information including attitude,velocity and position was obtained through the data from MEMS inertial measurement unit and strapdown inertial navigation algorithm.The azimuth of the navigation system was obtained by magnetic sensors.The errors of MEMS inertial navigation system and random errors of inertial sensors were modified by applying gait phase detection and zero-velocity update,so that the accumulate speed of the positioning errors was slowed down.The navigation experiment result shows that,the navigation errors of straight line and rectangle route retain about 2 m and 6 m during about 9 minutes’ walking.The errors take up 1.1% and 2.5% of the whole distances of walking respectively.The experiment conclusion proves that the proposed method can effectively improve the positioning precision of personal navigation system,and can achieve personal positioning for longer time under the environment that GNSS signal is attenuated or become failure.

Vision and IMU Data Fusion: Closed-Form Determination of the Absolute Scale, Speed and Attitude

Abstract: This chapter describes an algorithm for determining the speed and the attitude of a sensor assembling constituted by a monocular camera and inertial sensors (three orthogonal accelerometers and three orthogonal gyroscopes). The system moves in a 3D unknown environment. The algorithm inputs are the visual and inertial measurements during a very short time interval. The outputs are: the speed and attitude, the absolute scale and the bias affecting the inertial measurements. The determination of these outputs is obtained by a simple closed form solution which analytically expresses the previous physical quantities in terms of the sensor measurements. This closed form determination allows performing the overall estimation in a very short time interval and without the need of any initialization or prior knowledge. This is a key advantage since allows eliminating the drift on the absolute scale and on the orientation. The performance of the proposed algorithm is evaluated with real experiments.

Action trajectory reconstruction from inertial sensor measurements

Abstract: Inertial sensors, such as accelerometers and gyroscopes, are rarely used by themselves to compute velocity and position as each requires the integration of very noisy data. The variance and bias in the resulting position and velocity estimates grow unbounded in time. This paper proposes a solution to provide a de-biased and de-noised estimation of position and velocity of human actions from accelerometer measurements. The method uses a continuous wavelet transform applied to the measurements recursively to provide reliable action trajectory reconstruction. The results are presented from experiments performed with a MEMS accelerometer and gyroscope.