Sine Rotation Vector Method for Attitude Estimation of an Underwater Robot
Abstract
:1. Introduction
2. Problem Formulation
2.1. Nomenclature
attitude of a robot at time t; , where , and indicate roll, pitch and yaw | |
attitude estimated at time t through prediction and a correction procedure; | |
error covariance of the estimated attitude | |
attitude predicted at time t, before it is corrected by the measurements; | |
error covariance of the predicted attitude | |
acceleration measured in the instrument coordinate frame; | |
normalized acceleration measurement; | |
magnetic field measured in the instrument coordinate frame; | |
normalized magnetic field measurement; | |
measurements of roll, pitch and yaw calculated from the and at time t; | |
linear velocity of the robot in the robot coordinate frame; | |
rotational velocity of the robot in the robot coordinate frame; | |
motion model of a robot that relates the robot attitude and the linear velocity to the rotational velocity of the robot; | |
the measurement model that relates state to the measurement ; | |
the k-th discretized sampling time instant | |
time period between and ; |
2.2. Problem Formulation
3. Predictions of Attitude and Covariance
4. Corrections of Predicted Attitude and Covariance
5. Innovation by Sine Rotation Vector
5.1. Calculation of Innovation by Sine Rotation Vector
5.2. Sine Rotation Vector
6. Experiments and Results
6.1. Test through Simulation
6.2. Experiment in a Test Tank
6.3. Experiment on the Ground
7. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Short-Term Use | Long-Term Use | |
---|---|---|
Angular rate (gyroscope) | prediction | - |
Gravitational field (accelerometer) | - | correction |
Magnetic field (magnetometer) | - | correction |
Velocity (DVL) | - | (1) Location estimation |
(2) Rejection of dynamic effect | ||
in gravitational field measurement |
Simulation | Measurement | Uncertainty | Value (degree) |
---|---|---|---|
Simulation 1 | Acceleration | bias | |
random noise | |||
Magnetic field | bias | ||
random noise | |||
Simulation 2 | Acceleration | bias | |
random noise | |||
Magnetic field | bias | ||
random noise |
Simulation | Statistics | SRV | EKF | UKF | CF |
---|---|---|---|---|---|
Simulation 1 | Average (rad) | ||||
RMS (rad) | |||||
distance error ratio (%) | 1.689 | 1.639 | |||
Simulation 2 | Average (rad) | ||||
RMS (rad) | |||||
distance error ratio (%) | 1.489 | 0.617 |
Trajectory | Distance Error Ratio | |||
---|---|---|---|---|
SRV | EKF | UKF | CF | |
Circular trajectory | 1.08% | 2.38% | 2.68% | 3.37% |
Rectangular trajectory | 2.06% | 4.07% | 4.82% | 4.82% |
Statistics | Error (Rad) | |||
---|---|---|---|---|
SRV | EKF | UKF | CF | |
Average | 0.1264 | 0.2494 | 0.2505 | 0.2476 |
Root mean square error | 0.1862 | 0.2994 | 0.3001 | 0.3016 |
Maximum error | 0.4931 | 0.6749 | 0.6826 | 0.6877 |
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Ko, N.Y.; Jeong, S.; Bae, Y. Sine Rotation Vector Method for Attitude Estimation of an Underwater Robot. Sensors 2016, 16, 1213. https://doi.org/10.3390/s16081213
Ko NY, Jeong S, Bae Y. Sine Rotation Vector Method for Attitude Estimation of an Underwater Robot. Sensors. 2016; 16(8):1213. https://doi.org/10.3390/s16081213
Chicago/Turabian StyleKo, Nak Yong, Seokki Jeong, and Youngchul Bae. 2016. "Sine Rotation Vector Method for Attitude Estimation of an Underwater Robot" Sensors 16, no. 8: 1213. https://doi.org/10.3390/s16081213
APA StyleKo, N. Y., Jeong, S., & Bae, Y. (2016). Sine Rotation Vector Method for Attitude Estimation of an Underwater Robot. Sensors, 16(8), 1213. https://doi.org/10.3390/s16081213