Improving Observability of an Inertial System by Rotary Motions of an IMU

Du, Shuang; Sun, Wei; Gao, Yang

doi:10.3390/s17040698

Cited by 22 publications

(15 citation statements)

References 31 publications

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“…The inertial navigation community have studied previously the observability of IMU biases in different estimation frameworks [25], [26], [27]. It has been proven that in the presence of inertial data, the biases of the accelerometer are observable only if the attitude of the system is perfectly known or if the trajectory contains rotations [28].…”

Section: Bias Observabilitymentioning

confidence: 99%

IN2LAMA: INertial Lidar Localisation And MApping

Gentil

Vidal-Calleja

Huang

2019

2019 International Conference on Robotics and Automation (ICRA)

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In this paper, we present INertial Lidar Localisation Autocalibration And MApping (IN2LAAMA): a probabilistic framework for localisation, mapping, and extrinsic calibration based on a 3D-lidar and a 6-DoF-IMU. Most of today's lidars collect geometric information about the surrounding environment by sweeping lasers across their field of view. Consequently, 3Dpoints in one lidar scan are acquired at different timestamps. If the sensor trajectory is not accurately known, the scans are affected by the phenomenon known as motion distortion. The proposed method leverages preintegration with a continuous representation of the inertial measurements to characterise the system's motion at any point in time. It enables precise correction of the motion distortion without relying on any explicit motion model. The system's pose, velocity, biases, and time-shift are estimated via a full batch optimisation that includes automatically generated loop-closure constraints. The autcalibration and the registration of lidar data relies on planar and edge features matched across pairs of scans. The performance of the framework is validated through simulated and real-data experiments.

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Section: Bias Observabilitymentioning

confidence: 99%

IN2LAMA: INertial Lidar Localisation And MApping

Gentil

Vidal-Calleja

Huang

2019

2019 International Conference on Robotics and Automation (ICRA)

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“…quation (50) describes the relations between sensor biases in the sensor and navigation frames, as the roll, pitch, and azimuth are 0°, 0°, and 0°, respectively. Apparently, sensor biases in the east and north directions are combinations of sensor biases in X and Y axes.…”

Section: Imu Rotation About Z Axis When Pitch Is 0°ementioning

confidence: 99%

“…Although the simulation results indicate the error states can be partially estimated, a detailed observability analysis was not present in their work. Based on control theory, system observability was examined with IMU rotation about the X, Y, and Z axes, respectively . Through manipulation of the observability matrix, their work indicated that the azimuth error is the only unobservable state with proper IMU rotations.…”

Section: Introductionmentioning

confidence: 99%

Self‐alignment and calibration of a MEMS IMU: Improving observability and estimability by rotary motions of IMU

2019

Asian Journal of Control

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The stationary self-alignment and calibration (SSAC) for a low-cost MEMS IMU is quite challenging due to the poor observability of an inertial system under static condition and the significant sensor errors of MEMS inertial sensors. This research proposes to employ IMU rotations to improve the system observability and estimability regarding the SSAC of a low-cost MEMS IMU.IMU rotations about the X, Y, and Z axes are employed in this paper. The analytic estimation algorithm for each error state is derived and the observability of the system with IMU rotation is analyzed. As the observability analysis will not provide clues about how well an error state can be estimated, the estimability analysis is also conducted based on the eigenvalues and eigenvectors from the covariance matrix in the Kalman filter. Tests are conducted with a tri-axial turntable to verify the improvements on system observability and estimability brought by IMU rotations. Of both theoretical analysis and results indicated with proper IMU rotations, only azimuth error still remains unobservable, and the IMU rotation also significantly improves the estimability of all error states, including the unobservable azimuth.

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“…A system should choose a suitable rotation scheme based on the error characteristics of its own. References [21,22] have analyzed the error characteristics of MIMU rotating around the X -axis, Y -axis, and Z -axis respectively, and have studied the error observability of the inertial navigation system rotating in different directions. This is instructive for knowing which axis the INS selects as the modulation axis.…”

Section: Introductionmentioning

confidence: 99%

A Novel Rotation Scheme for MEMS IMU Error Mitigation Based on a Missile-Borne Rotation Semi-Strapdown Inertial Navigation System

Jing

Zhang

et al. 2019

Sensors

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In previous research, a semi-strapdown inertial navigation system (SSINS), based on micro-electro-mechanical system (MEMS) sensors, was able to realize over-range measurement of the attitude information of high-rotation missiles by constructing a single axis “spin reduction” platform. However, the MEMS sensors in SSINS were corrupted by significant sensor errors. In order to further improve SSINS measurement accuracy, a rotational modulation technique has been introduced to compensate for sensor errors. The ideal modulation angular velocity is changed sharply to achieve a constant speed, while in practical applications, the angular rate of the rotating mechanism’s output needs to go through an acceleration-deceleration process. Furthermore, the stability of the modulation angular rate is difficult to achieve in a high-speed rotation environment. In this paper, a novel rotation scheme is proposed which can effectively suppress the residual error in the navigation coordinate system caused by the modulation angular rate error, including the acceleration-deceleration process and instability of angular rate. The experiment results show that the position and attitude accuracy of the new rotation scheme was increased by more than 56%. In addition, the proposed scheme is applicable to navigation accuracy improvement under various dynamic conditions.

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Improving Observability of an Inertial System by Rotary Motions of an IMU

Cited by 22 publications

References 31 publications

IN2LAMA: INertial Lidar Localisation And MApping

IN2LAMA: INertial Lidar Localisation And MApping

Self‐alignment and calibration of a MEMS IMU: Improving observability and estimability by rotary motions of IMU

A Novel Rotation Scheme for MEMS IMU Error Mitigation Based on a Missile-Borne Rotation Semi-Strapdown Inertial Navigation System

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