Showing papers on "Inertial measurement unit published in 2008"

Analysis and Modeling of Inertial Sensors Using Allan Variance

Abstract: It is well known that inertial navigation systems can provide high-accuracy position, velocity, and attitude information over short time periods. However, their accuracy rapidly degrades with time. The requirements for an accurate estimation of navigation information necessitate the modeling of the sensors' error components. Several variance techniques have been devised for stochastic modeling of the error of inertial sensors. They are basically very similar and primarily differ in that various signal processings, by way of weighting functions, window functions, etc., are incorporated into the analysis algorithms in order to achieve a particular desired result for improving the model characterizations. The simplest is the Allan variance. The Allan variance is a method of representing the root means square (RMS) random-drift error as a function of averaging time. It is simple to compute and relatively simple to interpret and understand. The Allan variance method can be used to determine the characteristics of the underlying random processes that give rise to the data noise. This technique can be used to characterize various types of error terms in the inertial-sensor data by performing certain operations on the entire length of data. In this paper, the Allan variance technique will be used in analyzing and modeling the error of the inertial sensors used in different grades of the inertial measurement units. By performing a simple operation on the entire length of data, a characteristic curve is obtained whose inspection provides a systematic characterization of various random errors contained in the inertial-sensor output data. Being a directly measurable quantity, the Allan variance can provide information on the types and magnitude of the various error terms. This paper covers both the theoretical basis for the Allan variance for modeling the inertial sensors' error terms and its implementation in modeling different grades of inertial sensors.

A complementary filter for attitude estimation of a fixed-wing UAV

Abstract: This paper considers the question of using a nonlinear complementary filter for attitude estimation of fixed-wing unmanned aerial vehicle (UAV) given only measurements from a low-cost inertial measurement unit. A nonlinear complementary filter is proposed that combines accelerometer output for low frequency attitude estimation with integrated gyrometer output for high frequency estimation. The raw accelerometer output includes a component corresponding to airframe acceleration, occurring primarily when the aircraft turns, as well as the gravitational acceleration that is required for the filter. The airframe acceleration is estimated using a simple centripetal force model (based on additional airspeed measurements), augmented by a first order dynamic model for angle-of-attack, and used to obtain estimates of the gravitational direction independent of the airplane manoeuvres. Experimental results are provided on a real-world data set and the performance of the filter is evaluated against the output from a full GPS/INS that was available for the data set.

Modelling, Identification and Control of a Quadrotor Helicopter

Abstract: This thesis work focused on the study of a quadrotor helicopter. The dynamic system modelling and the control algorithm evaluation were carried out. To test the results, a simulator and a real platform were developed. The Newton-Euler formalism was used to model the dynamic system. Particular attention was given to the group composed of the DC-motor, the gear box and the propeller which needed also the estimation of aerodynamic lift and torque to reach better accuracy. PID control algorithms were compared. The first stage tests were performed on a simulated model where it was easy to evaluate the performance with a mathematical approach. The second stage tests were carried out on the quadrotor platform to evaluate the behavior of the real system. A simulator based on Matlab-Simulink was developed. With this program it was possible to test the accuracy of the model and the robustness of the control algorithms. Furthermore a 3D graphic output and a joystick interface made easier the testability and the observability of the system. A quadrotor platform was developed. The electronics was composed of a Micro Control Unit (MCU) interfaced with several devices: the power supply, the receiving unit, the DC-motor power boards, the Inertial Measurement Unit (IMU), the SONAR and the IR modules. Thanks to these devices and the MCU software, both guided and autonomous flights were possible. (Less)

A Kalman Filter-Based Algorithm for IMU-Camera Calibration: Observability Analysis and Performance Evaluation

Abstract: Vision-aided inertial navigation systems (V-INSs) can provide precise state estimates for the 3-D motion of a vehicle when no external references (e.g., GPS) are available. This is achieved by combining inertial measurements from an inertial measurement unit (IMU) with visual observations from a camera under the assumption that the rigid transformation between the two sensors is known. Errors in the IMU-camera extrinsic calibration process cause biases that reduce the estimation accuracy and can even lead to divergence of any estimator processing the measurements from both sensors. In this paper, we present an extended Kalman filter for precisely determining the unknown transformation between a camera and an IMU. Contrary to previous approaches, we explicitly account for the time correlation of the IMU measurements and provide a figure of merit (covariance) for the estimated transformation. The proposed method does not require any special hardware (such as spin table or 3-D laser scanner) except a calibration target. Furthermore, we employ the observability rank criterion based on Lie derivatives and prove that the nonlinear system describing the IMU-camera calibration process is observable. Simulation and experimental results are presented that validate the proposed method and quantify its accuracy.

A Practical Visual Servo Control for an Unmanned Aerial Vehicle

Abstract: An image-based visual servo control is presented for an unmanned aerial vehicle (UAV) capable of stationary or quasi-stationary flight with the camera mounted onboard the vehicle. The target considered consists of a finite set of stationary and disjoint points lying in a plane. Control of the position and orientation dynamics is decoupled using a visual error based on spherical centroid data, along with estimations of the linear velocity and the gravitational inertial direction extracted from image features and an embedded inertial measurement unit. The visual error used compensates for poor conditioning of the image Jacobian matrix by introducing a nonhomogeneous gain term adapted to the visual sensitivity of the error measurements. A nonlinear controller, that ensures exponential convergence of the system considered, is derived for the full dynamics of the system using control Lyapunov function design techniques. Experimental results on a quadrotor UAV, developed by the French Atomic Energy Commission, demonstrate the robustness and performance of the proposed control strategy.

A Simplified Quaternion-Based Algorithm for Orientation Estimation From Earth Gravity and Magnetic Field Measurements

Abstract: Orientation of a static or slow-moving rigid body can be determined from the measured gravity and local magnetic field vectors. Some formulation of the QUaternion ESTimator (QUEST) algorithm is commonly used to solve this problem. Triads of accelerometers and magnetometers are used to measure gravity and local magnetic field vectors in sensor coordinates. In the QUEST algorithm, local magnetic field measurements affect not only the estimation of yaw but also that of roll and pitch. Due to the deviations in the direction of the magnetic field vector between locations, it is not desirable to use magnetic data in calculations that are related to the determination of roll and pitch. This paper presents a geometrically intuitive 3-degree-of-freedom (3-DOF) orientation estimation algorithm with physical meaning [which is called the factored quaternion algorithm (FQA)], which restricts the use of magnetic data to the determination of the rotation about the vertical axis. The algorithm produces a quaternion output to represent the orientation. Through a derivation based on half-angle formulas and due to the use of quaternions, the computational cost of evaluating trigonometric functions is avoided. Experimental results demonstrate that the proposed algorithm has an overall accuracy that is essentially identical to that of the QUEST algorithm and is computationally more efficient. Additionally, magnetic variations cause only azimuth errors in FQA attitude estimation. A singularity avoidance method is introduced, which allows the algorithm to track through all orientations.

Foot mounted inertial system for pedestrian navigation

Abstract: This paper discusses algorithmic concepts, design and testing of a system based on a low-cost MEMS-based inertial measurement unit (IMU) and high-sensitivity global positioning system (HSGPS) receivers for seamless personal navigation in a GPS signal degraded environment. The system developed here is mounted on a pedestrian shoe/foot and uses measurements based on the dynamics experienced by the inertial sensors on the user's foot. The IMU measurements are processed through a conventional inertial navigation system (INS) algorithm and are then integrated with HSGPS receiver measurements and dynamics derived constraint measurements using a tightly coupled integration strategy. The ability of INS to bridge the navigation solution is evaluated through field tests conducted indoors and in severely signal degraded forest environments. The specific focus is on evaluating system performance under challenging GPS conditions.

Methods for in-field user calibration of an inertial measurement unit without external equipment

Abstract: This paper presents methods to calibrate and compensate for non-zero biases, non-unit scale factors, axis misalignments and cross-axis sensitivities of both the tri-axial accelerometer and gyroscopic setups in a micro-electro-mechanical systems (MEMS) based inertial measurement unit (IMU). These methods depend on the Earth's gravity as a stable physical calibration standard. Specifically, the calibration of gyroscopes is significantly improved by comparing the outputs of the accelerometer and the IMU orientation integration algorithm, after arbitrary motions. The derived property and proposed cost function allow the gyroscopes to be calibrated without external equipment, such as a turntable, or requiring precise maneuvers. Both factors allow the IMU to be easily calibrated by the user in the field so that it can function as an accurate orientation sensor. A custom-made prototype IMU is used to demonstrate the effectiveness of the proposed methods, with data that are carefully obtained using prescribed motions, as well as those less rigorously collected from the IMU when it is mounted on the head of a user or held in hands with brief random movements. With calibration, the observed average static angular error is less than a quarter of a degree and the dynamic angular error is reduced by a factor of 2 to 5.

An Integrated UAV Navigation System Based on Aerial Image Matching

Abstract: The aim of this paper is to explore the possibility of using geo-referenced satellite or aerial images to augment an Unmanned Aerial Vehicle (UAV) navigation system in case of GPS failure. A vision based navigation system which combines inertial sensors, visual odometer and registration of a UAV on-board video to a given geo-referenced aerial image has been developed and tested on real flight-test data. The experimental results show that it is possible to extract useful position information from aerial imagery even when the UAV is flying at low altitude. It is shown that such information can be used in an automated way to compensate the drift of the UAV state estimation which occurs when only inertial sensors and visual odometer are used.

Fusion motion capture: A prototype system using inertial measurement units and GPS for the biomechanical analysis of ski racing

Abstract: In this pilot study fusion motion capture (FMC) has been used to capture 3‐D kinetics and kinematics of alpine ski racing. The new technology has overcome the technological difficulties associated with athlete performance monitoring in an alpine environment. FMC is a general term to describe motion capture when several different streams of data are fused to measure athlete motion. In this article inertial measurement units (IMU), global positioning system (GPS) pressure sensitive insoles, video and theodolite measurements have been combined. The core of the FMC is the fusion of IMU and GPS data. IMU may contain accelerometers, gyroscopes, magnetometers and a thermometer, and they track local orientation and acceleration of each limb segment of interest. GPS data are fused with local acceleration data to track the global trajectory of the athlete. Fusion integration algorithms designed by the authors [1] were used to improve the accuracy of the independent Kalman filter solutions provided by the vendors of...

Self-contained inertial navigation system for interactive control using movable controllers

Abstract: A movable game controller for controlling aspects of a computer controlled game display with apparatus for determining the linear and angular motion of that movable controller. The apparatus includes a plurality of self-contained inertial sensors for sensing the tri-axial linear and tri-axial angular motion of the moving controller. Each sensor is mounted at a fixed linear position and orientation with respect to the others. The linear and angular motion of the controller is computed from the correlated motion sensor readings of each of the plurality of self-contained inertial sensors.

A Standard Testing and Calibration Procedure for Low Cost MEMS Inertial Sensors and Units

Abstract: Navigation involves the integration of methodologies and systems for estimating the time varying position and attitude of moving objects. Inertial Navigation Systems (INS) and the Global Positioning System (GPS) are among the most widely used navigation systems. The use of cost effective MEMS based inertial sensors has made GPS/INS integrated navigation systems more affordable. However MEMS sensors suffer from various errors that have to be calibrated and compensated to get acceptable navigation results. Moreover the performance characteristics of these sensors are highly dependent on the environmental conditions such as temperature variations. Hence there is a need for the development of accurate, reliable and efficient thermal models to reduce the effect of these errors that can potentially degrade the system performance. In this paper, the Allan variance method is used to characterize the noise in the MEMS sensors. A six-position calibration method is applied to estimate the deterministic sensor errors such as bias, scale factor, and non-orthogonality. An efficient thermal variation model is proposed and the effectiveness of the proposed calibration methods is investigated through a kinematic van test using integrated GPS and MEMS-based inertial measurement unit (IMU).

Hovering flight and vertical landing control of a VTOL Unmanned Aerial Vehicle using optical flow

Abstract: This paper presents a nonlinear controller for hovering flight and touchdown control for a vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV) using inertial optical flow. The VTOL vehicle is assumed to be a rigid body, equipped with a minimum sensor suite (camera and IMU), manoeuvring over a textured flat target plane. Two different tasks are considered in this paper: the first concerns the stability of hovering flight and the second one concerns regulation of automatic landing using the divergent optical flow as feedback information. Experimental results on a quad-rotor UAV demonstrate the performance of the proposed control strategy.

Map-Based Precision Vehicle Localization in Urban Environments

Abstract: Many urban navigation applications (eg, autonomous navigation, driver assistance systems) can benefit greatly from localization with centimeter accuracy Yet such accuracy cannot be achieved reliably with GPS-based inertial guidance systems, specifically in urban settings We propose a technique for high-accuracy localization of moving vehicles that utilizes maps of urban environments Our approach integrates GPS, IMU, wheel odometry, and LIDAR data acquired by an instrumented vehicle, to generate high-resolution environment maps Offline relaxation techniques similar to recent SLAM methods [2, 10, 13, 14, 21, 30] are employed to bring the map into alignment at intersections and other regions of self-overlap By reducing the final map to the flat road surface, imprints of other vehicles are removed The result is a 2-D surface image of ground reflectivity in the infrared spectrum with 5cm pixel resolution To localize a moving vehicle relative to these maps, we present a particle filter method for correlating LIDAR measurements with this map As we show by experimentation, the resulting relative accuracies exceed that of conventional GPS-IMU-odometry-based methods by more than an order of magnitude Specifically, we show that our algorithm is effective in urban environments, achieving reliable real-time localization with accuracy in the 10- centimeter range Experimental results are provided for localization in GPS-denied environments, during bad weather, and in dense traffic The proposed approach has been used successfully for steering a car through narrow, dynamic urban roads

Hybrid tracking of human operators using IMU/UWB data fusion by a Kalman filter

Abstract: The precise localization of human operators in robotic workplaces is an important requirement to be satisfied in order to develop human-robot interaction tasks. Human tracking provides not only safety for human operators, but also context information for intelligent human-robot collaboration. This paper evaluates an inertial motion capture system which registers full-body movements of an user in a robotic manipulator workplace. However, the presence of errors in the global translational measurements returned by this system has led to the need of using another localization system, based on Ultra-WideBand (UWB) technology. A Kalman filter fusion algorithm which combines the measurements of these systems is developed. This algorithm unifies the advantages of both technologies: high data rates from the motion capture system and global translational precision from the UWB localization system. The developed hybrid system not only tracks the movements of all limbs of the user as previous motion capture systems, but is also able to position precisely the user in the environment.

A Dynamic-Model-Based Wheel Slip Detector for Mobile Robots on Outdoor Terrain

Abstract: This paper introduces a model-based approach to estimating longitudinal wheel slip and detecting immobilized conditions of autonomous mobile robots operating on outdoor terrain. A novel tire traction/braking model is presented and used to calculate vehicle dynamic forces in an extended Kalman filter framework. Estimates of external forces and robot velocity are derived using measurements from wheel encoders, inertial measurement unit, and GPS. Weak constraints are used to constrain the evolution of the resistive force estimate based upon physical reasoning. Experimental results show the technique accurately and rapidly detects robot immobilization conditions while providing estimates of the robot's velocity during normal driving. Immobilization detection is shown to be robust to uncertainty in tire model parameters. Accurate immobilization detection is demonstrated in the absence of GPS, indicating the algorithm is applicable for both terrestrial applications and space robotics.

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

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

Ultrasound-aided pedestrian dead reckoning for indoor navigation

Abstract: Ad hoc solutions for tracking and providing navigation support to emergency response teams is an important and safety-critical challenge. We propose a navigation system based on a combination of foot-mounted inertial sensors and ultrasound beacons. We evaluate experimentally the performance of our dead reckoning system in different environments and for different trail topologies. The inherent drift observed in dead reckoning is addressed by deploying ultrasound beacons as landmarks. We study through simulations the use of the proposed approach in guiding a person along a defined path.Simulation results show that satisfactory guidance performance is achieved despite noisy ultrasound measurements, magnetic interference and uncertainty in ultrasound node locations. The models used for the simulations are based on experimental data and the authors' experience with actual sensors. The simulation results will be used to inform future development of a full real time system.

Integration of foot-mounted inertial sensors into a Bayesian location estimation framework

Abstract: An algorithm for integrating foot-mounted inertial sensors into a Bayesian location estimation framework is presented. The proposed integration scheme is based on a cascaded estimation architecture. A lower Kalman filter is used to estimate the step-wise change of position and direction of the foot. These estimates are used in turn as measurements in an upper particle filter, which is able to incorporate nonlinear map-matching techniques. Experimental data is used to verify the proposed algorithm.

Wireless MEMS inertial sensor system for golf swing dynamics

Abstract: The emergence of accurate MEMS inertial sensors motivates the design of miniature inertial measurement units (IMU) for applications well outside the field of inertial navigation. One promising application concerns novel sports training systems with inertial sensors embedded directly in sports equipment. This paper describes the theory, design, and evaluation of a miniature, wireless IMU that precisely measures the dynamics of a golf club used in putting. The design consists of a complete six degree-of-freedom IMU composed of MEMS accelerometers and angular rate gyros with an integrated microprocessor and RF transceiver. The resulting sensor system has negligible mass (25 g relative to 490 g for the putter) and is a mere 13 mm in diameter allowing it to fit wholly within the shaft of the club at the grip end. The measurement theory enables the computation of the position, velocity, and orientation of the club head at the opposite end of the shaft during the entire putting stroke. Experiments reveal that the three-dimensional position and orientation of the club head can be resolved to within 3 mm and 0.5°, respectively. These achievements yield a highly accurate, portable, and inexpensive sensor system to support golf swing training, custom club fitting, and club design.

Intelligent shoes for abnormal gait detection

Abstract: In this paper we introduce a shoe-integrated system for human abnormal gait detection. This intelligent system focuses on detecting the following patterns: normal gait, toe in, toe out, oversupination, and heel walking gait abnormalities. An inertial measurement unit (IMU) consisting of three-dimensional gyroscopes and accelerometers is employed to measure angular velocities and accelerations of the foot. Four force sensing resistors (FSRs) and one bend sensor are installed on the insole of each foot for force and flexion information acquisition. The proposed detection method is mainly based on Principal Component Analysis (PCA) for feature generation and Support Vector Machine (SVM) for multi-pattern classification. In the present study, four subjects tested the shoe-integrated device in outdoor environments. Experimental results demonstrate that the proposed approach is robust and efficient in detecting abnormal gait patterns. Our goal is to provide a cost-effective system for detecting gait abnormalities in order to assist persons with abnormal gaits in the developing of a normal walking pattern in their daily life.

Cascaded estimation architecture for integration of foot-mounted inertial sensors

Abstract: An algorithm for integrating foot-mounted inertial sensor platforms is presented. The proposed integration scheme is based on a cascaded estimation architecture. A lower Kalman filter is used to estimate the step-wise change of position and direction of one or optionally both feet respectively. These estimates are used in turn as measurements in an upper particle filter, which is able to incorporate nonlinear map-matching techniques. To ease the integration of both feet a simple mechanical pedestrian model is developed. The proposed algorithm is verified using computer simulations and experimental data.

Drift-free dynamic height sensor using MEMS IMU aided by MEMS pressure sensor

Abstract: We demonstrate a low-cost, low-power, and small form factor solution to drift-free high-resolution vertical positioning by fusing MEMS accelerometers with MEMS barometric altimeter. In this system, the highly responsive but drift-prone aspect of the MEMS accelerometers is stabilized by barometric altimeter and high-fidelity height tracking is achieved. Typical vertical human movements such as walking up or down a staircase can be tracked in real-time with this system. The height tracking performance is benchmarked against a reference system using a tactical-grade IMU and an error analysis is performed.

Calibration and Stochastic Modelling of Inertial Navigation Sensor Erros

Abstract: The integration of Global Positioning System (GPS) with an inertial measurement unit (IMU) has been widely used in many applications of positioning and orientation. The performance of a GPS-aided inertial integrated navigation system is mainly characterized by the ability of the IMU to bridge GPS outages. This basically depends on the inertial sensor errors that cause a rapid degradation in the integrated navigation solution during periods of GPS outages. The inertial sensor errors comprise systematic and random components. In general, systematic errors (deterministic) can be estimated by calibration and therefore they can be removed from the raw observations. Random errors can be studied by linear or high order nonlinear stochastic processes. These stochastic models can be utilized by a navigation filter such as, Kalman filter, to provide optimized estimation of navigation parameters. Traditionally, random constant (RC), random walk (RW), Gauss-Markov (GM), and autoregressive (AR) processes have been used to develop the stochastic model in the navigation filters. In this technical note, the inertial sensor errors are introduced and discussed. Subsequently, a six-position laboratory calibration test is described. Then, mathematical models for RC, RW, GM, and AR stochastic models with associated variances for gyros and accelerometer random errors are presented along with a discussion regarding ongoing research in this field. Also, the implementation of a stochastic model in a loosely coupled INS/GPS navigation filter is explained.

Combined Visual and Inertial Navigation for an Unmanned Aerial Vehicle

Abstract: We describe an UAV navigation system which combines stereo visual odometry with inertial measurements from an IMU. Our approach fuses the motion estimates from both sensors in an extended Kalman filter to determine vehicle position and attitude. We present results using data from a robotic helicopter, in which the visual and inertial system produced a final position estimate within 1% of the measured GPS position, over a flight distance of more than 400 meters. Our results show that the combination of visual and inertial sensing reduced overall positioning error by nearly an order of magnitude compared to visual odometry alone.

Joint angle tracking with inertial sensors

Abstract: Many wearable inertial systems have been used to continuously track human movement in and outside of a laboratory. The number of sensors and the complexity of the algorithms used to measure position and orientation vary according to the clinical application. To calculate changes in orientation, researchers often integrate the angular velocity. However, a relatively small error in measured angular velocity leads to large integration errors. This restricts the time of accurate measurement to a few minutes. We have combined kinematic models designed for control of robotic arms with state space methods to directly and continuously estimate the joint angles from inertial sensors. These algorithms can be applied to any combination of sensors, can easily handle malfunctions or the loss of some sensor inputs, and can be used in either a real-time or an off-line processing mode with higher accuracy.

Dynamic accuracy of inertial measurement units during simple pendulum motion.

Abstract: A motion measurement system based on inertial measurement units (IMUs) has been suggested as an alternative to contemporary video motion capture. This paper reports an investigation into the accuracy of IMUs in estimating 3D orientation during simple pendulum motion. The IMU vendor's (XSens Technologies) accuracy claim of 3 degrees root mean squared (RMS) error is tested. IMUs are integrated electronic devices that contain accelerometers, magnetometers and gyroscopes. The motion of a pendulum swing was measured using both IMUs and video motion capture as a reference. The IMU raw data were processed by the Kalman filter algorithm supplied by the vendor and a custom fusion algorithm developed by the authors. The IMU measurement of pendulum motion using the vendor's Kalman filter algorithm did not compare well with the video motion capture with a RMS error of between 8.5 degrees and 11.7 degrees depending on the length and type of pendulum swing. The maximum orientation error was greater than 30 degrees , occurring approximately eight seconds into the motion. The custom fusion algorithm estimation of orientation compared well with the video motion capture with a RMS error of between 0.8 degrees and 1.3 degrees . Future research should concentrate on developing a general purpose fusion algorithm and vendors of IMUs should provide details about the errors to be expected in different measurement situations, not just those in a 'best case' scenario.

The static accuracy and calibration of inertial measurement units for 3D orientation

Abstract: Inertial measurement units (IMUs) are integrated electronic devices that contain accelerometers, magnetometers and gyroscopes. Wearable motion capture systems based on IMUs have been advertised as alternatives to optical motion capture. In this paper, the accuracy of five different IMUs of the same type in measuring 3D orientation in static situations, as well as the calibration of the accelerometers and magnetometers within the IMUs, has been investigated. The maximum absolute static orientation error was 5.2 degrees , higher than the 1 degrees claimed by the vendor. If the IMUs are re-calibrated at the time of measurement with the re-calibration procedure described in this paper, it is possible to obtain an error of less than 1 degrees , in agreement with the vendor's specifications (XSens Technologies B.V. 2005. Motion tracker technical documentation Mtx-B. Version 1.03. Available from: www.xsens.com). The new calibration appears to be valid for at least 22 days providing the sensor is not exposed to high impacts. However, if several sensors are 'daisy chained' together changes to the magnetometer bias can cause heading errors of up to 15 degrees . The results demonstrate the non-linear relationship between the vendor's orthogonality claim of < 0.1 degrees and the accuracy of 3D orientation obtained from factory calibrated IMUs in static situations. The authors hypothesise that the high magnetic dip (64 degrees ) in our laboratory may have exacerbated the errors reported. For biomechanical research, small relative movements of a body segment from a calibrated position are likely to be more accurate than large scale global motion that may have an error of up to 9.8 degrees .

Civilian Vehicle Navigation: Required Alignment of the Inertial Sensors for Acceptable Navigation Accuracies

Abstract: A vital necessity for any kind of inertial navigation system (INS) is the alignment of its axis with the vehicle body frame (VBF). Civilian vehicle navigation has strict requirements with respect to cost, size, reliability, and ease of implementation of the system. Microelectromechanical system (MEMS) inertial sensors have satisfied the cost and size requirements for civilian vehicle navigation; however, reliability and ease of implementation of these low-cost and miniaturized navigation systems are still parts of major research and investigation. This paper focuses on an important aspect of the ease of implementation for inertial sensors. From a civilian user perspective, accurately aligning the inertial system with respect to the vehicle, before every use, is not a desirable quality for a portable navigation system. In addition, it is not realistic to assume that even a careful user can achieve good alignment accuracy of the system. The purpose of this paper is to investigate the effects of misalignment errors that will produce errors in initial alignment and affect the navigation accuracy for two different inertial systems. The inertial systems are classified according to the number of sensors used in the system. The first system consists of three gyros and three accelerometers [full inertial measurement unit (IMU)], whereas the second system only has one gyro and two horizontal accelerometers (partial IMU).