NR-UIO: NLOS-Robust UWB-Inertial Odometry Based on Interacting Multiple Model and NLOS Factor Estimation
Abstract
:1. Introduction
2. System Description
3. Tightly Coupled UWB-IMU IMM Framework
3.1. System Overview
3.1.1. Basic Definitions
3.1.2. System Overview
- (1)
- When the robot approaches the j-th UWB anchor and the range data are received, the initial anchor position estimation described in Section 3.2 is activated to estimate the initial position of the UWB anchor. This process works only once for the initial range data input, and the estimated UWB anchor position is subsequently included in the initial state vector of the system model. Here, it is assumed that the UWB anchor is mostly fixed to the floor or the ground and hardly changes its position over time.
- (2)
- Estimate the NLOS factor of the UWB raw range input through the IMU/UWB alignment process detailed in Section 3.5. The UKF is employed to estimate the NLOS factor. From the NLOS factor, if it is determined that the UWB range input data are from the NLOS situation, then the data can be removed or compensated to increase the robustness in the NLOS situation. The measurement, , selected through this process is used in the measurement update step.
- (3)
- Perform a time update for each system mode. When a series of UWB range data is obtained at time k and , respectively, the state and covariance are updated by performing state propagation for each mode as in Section 3.4. Perform the IMU pre-integration described in Section 3.3 for IMU sensor measurements accumulated between this time interval. In this process, the scale factor and the bias of the gyroscope and the bias of the accelerometer are included in the state vector to correct IMU measurements.
- (4)
- The measurement update step updates the state and covariance of each mode. In addition, the mode probability of each mode is calculated. Perform a combination operation of each mode probability and mode state to determine the final estimated state and covariance of the system.
3.2. Initial UWB Anchor Position Estimation
3.3. IMU Pre-Integration
3.4. Mode Time Update Process
3.4.1. Constant Velocity Model
3.4.2. Coordinated Turn Model
3.4.3. Mode Probability Update
3.5. UWB NLOS Factor Estimation
4. Experimental Results
4.1. Evaluation Test of the Proposed Algorithm
4.2. Comparison with State-of-the-Art Method
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Tag 1 | Tag 2 | Tag 3 | Tag 4 | |
---|---|---|---|---|
x-axis (m) | 0.4670 | −0.2050 | −0.4950 | 0.2120 |
y-axis (m) | −0.1810 | −0.4880 | 0.1880 | 0.4820 |
EKF | UIO | NR-UIO | |
---|---|---|---|
RMSE (m) | 1.3779 | 0.4748 | 0.2133 |
OC-UIO | NR-UIO | |
---|---|---|
RMSE (m) | 0.3086 | 0.2133 |
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Hyun, J.; Myung, H. NR-UIO: NLOS-Robust UWB-Inertial Odometry Based on Interacting Multiple Model and NLOS Factor Estimation. Sensors 2021, 21, 7886. https://doi.org/10.3390/s21237886
Hyun J, Myung H. NR-UIO: NLOS-Robust UWB-Inertial Odometry Based on Interacting Multiple Model and NLOS Factor Estimation. Sensors. 2021; 21(23):7886. https://doi.org/10.3390/s21237886
Chicago/Turabian StyleHyun, Jieum, and Hyun Myung. 2021. "NR-UIO: NLOS-Robust UWB-Inertial Odometry Based on Interacting Multiple Model and NLOS Factor Estimation" Sensors 21, no. 23: 7886. https://doi.org/10.3390/s21237886
APA StyleHyun, J., & Myung, H. (2021). NR-UIO: NLOS-Robust UWB-Inertial Odometry Based on Interacting Multiple Model and NLOS Factor Estimation. Sensors, 21(23), 7886. https://doi.org/10.3390/s21237886