State Derivation of a 12-Axis Gyroscope-Free Inertial Measurement Unit

Lu, Jau-Ching; Lin, Pei-Chun

doi:10.3390/s110303145

Cited by 10 publications

(9 citation statements)

References 26 publications

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“…Knowing the constant matrix N and receiving the accelerometer measurements as matrix F, the vector y can be determined based on (7) and 14- (16). By integrating α from (15) the angular velocity can be evaluated.…”

Section: Gyro-free Navigationmentioning

confidence: 99%

Improvement of angular velocity and position estimation in gyro‐free inertial navigation based on vision aid equipment

Dehghani

Kharrati

Seyedarabi

et al. 2017

IET Computer Vision

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In conventional navigation systems, inertial sensors consist of accelerometers and gyroscopes. These sensors suffer from in-built errors, accumulated drift and high-level noise sensitivity. The accurate gyroscopes are expensive and not suitable in cost-effective applications. To minimise such disadvantages, one solution is the combination of inertial sensors with different aiding sensors. To lower the cost, utilisation of redundant accelerometer structure as gyro-free inertial measurement unit (GFIMU) has been proposed. In this study, Gyro-Free navigation errors using four tri-axial accelerometers are illustrated. Compensation of errors in terms of angular velocity and position estimation is verified based on adding a simple gyroscope, inexpensive stereo cameras as well as creating an easy to use topological map. The topological map is easily created by means of scale-invariant feature transform method. The estimation of angular velocity is corrected on the basis of fusing the measurements from GFIMU and a simple gyroscope using unscented Kalman filter. The correction of position is performed by comparing the estimated position from GFIMU and observation of stereo cameras together with topological map. The results of the research show that the collaboration of GFIMU, stereo cameras and simple gyroscope will improve the robustness and accuracy of navigation, significantly.

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Section: Gyro-free Navigationmentioning

confidence: 99%

Improvement of angular velocity and position estimation in gyro‐free inertial navigation based on vision aid equipment

Dehghani

Kharrati

Seyedarabi

et al. 2017

IET Computer Vision

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“…When the CoM moves during a walking experiment (the position can be correctly estimated by the joint angles), its translational state can be computed from the IMU translational state plus the effect of angular acceleration ( i.e. , can be computed kinematically by the 12-axis IMU mount on the robot [20]) and angular velocity ( i.e. , by the gyro).…”

Section: The Body State Estimatormentioning

confidence: 99%

“…An IMU/encoder fusion model-based feedback control was also developed for improving the stability of the robot’s jogging motion [18]. In addition, a 9-axis IMU [19] and a 12-axis gyroscope-free IMU [20] were developed for better motion state reconstruction.…”

Section: Introductionmentioning

confidence: 99%

Sensor Data Fusion for Body State Estimation in a Bipedal Robot and Its Feedback Control Application for Stable Walking

Chen

Huang

et al. 2015

Sensors

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We report on a sensor data fusion algorithm via an extended Kalman filter for estimating the spatial motion of a bipedal robot. Through fusing the sensory information from joint encoders, a 6-axis inertial measurement unit and a 2-axis inclinometer, the robot’s body state at a specific fixed position can be yielded. This position is also equal to the CoM when the robot is in the standing posture suggested by the detailed CAD model of the robot. In addition, this body state is further utilized to provide sensory information for feedback control on a bipedal robot with walking gait. The overall control strategy includes the proposed body state estimator as well as the damping controller, which regulates the body position state of the robot in real-time based on instant and historical position tracking errors. Moreover, a posture corrector for reducing unwanted torque during motion is addressed. The body state estimator and the feedback control structure are implemented in a child-size bipedal robot and the performance is experimentally evaluated.

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“…Of course, drift errors are inevitable. To avoid these, the quadratic terms of the angular velocity can be used to obtain the magnitude of the rotation while only the direction is determined involving the integrated angular acceleration [34]- [37]. In contrast to this, recursive Bayesian filters, like the Kalman filter, allow to recover the magnitude and direction of the rotation jointly.…”

Section: Related Workmentioning

confidence: 99%

Observing Relative Motion With Three Accelerometer Triads

Schopp

Graf

Maurer

et al. 2014

IEEE Trans. Instrum. Meas.

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A gyroscope-free inertial measurement unit (GF-IMU) detects the relative motion of a body based on acceleration measurements only. It consists of multiple transducers attached at distinct locations within the body that together form an accelerometer array. In this paper, we employ only three accelerometer triads in order to completely capture the transversal and angular acceleration as well as the angular velocity. By modeling the GF-IMU as a nonlinear control system, we are able to conduct an observability analysis, which shows that this approach is capable of capturing an arbitrary spatial motion. We also show that additional triads only provide redundant information. Based on the control system formulation, we derive the models required to employ a nonlinear Kalman filter as a state observer. As the system description is of a general form they are suitable for any accelerometer array regardless of the number and placement of the transducers. Hence, the presented Kalman filter approach is applicable to all observable GF-IMU configurations. The measurements taken with a prototype on a 3-D rotation table confirm the observability analysis. The evaluations also show that the approach using three accelerometer triads achieves an estimation accuracy comparable with that of arrays employing a higher number of triads.

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State Derivation of a 12-Axis Gyroscope-Free Inertial Measurement Unit

Cited by 10 publications

References 26 publications

Improvement of angular velocity and position estimation in gyro‐free inertial navigation based on vision aid equipment

Improvement of angular velocity and position estimation in gyro‐free inertial navigation based on vision aid equipment

Sensor Data Fusion for Body State Estimation in a Bipedal Robot and Its Feedback Control Application for Stable Walking

Observing Relative Motion With Three Accelerometer Triads

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