Consistent Monocular Ackermann Visual–Inertial Odometry for Intelligent and Connected Vehicle Localization
Abstract
:1. Introduction
- (1)
- Additional analyses of different parameter configurations of ACK-MSCKF are performed with more real-world experiments.
- (2)
- Conducting the formulation and implementation of a consistent monocular Ackermann VIO, MAVIO, which not only improves the observability of the VIO scale direction but also resolves the inconsistency problem of ACK-MSCKF for further improving the positioning accuracy.
- (3)
- Introducing the raw GNSS error measurement model, MAVIO-GNSS, which further improves the vehicle positioning accuracy under the long-distance driving state.
- (4)
- The performance of MAVIO and MAVIO-GNSS are comprehensively compared with S-MSCKF and ACK-MSCKF on real-world datasets with twenty rounds, on average, of real-world experiments.
- (5)
- The source code [50] of MAVIO is publicly available to facilitate the reproducibility of related research.
2. The Proposed Approach
2.1. Coordinate Systems and Notations
- (1)
- Inertial Coordinate System of IMU {GI}. The origin of {GI} is the same as that of {I} at the time of VIO initialization. The axes of {GI} are obtained by calculation at the time of VIO initialization, and its z-axis is aligned with Earth’s gravity.
- (2)
- Inertial Coordinate System of Vehicle {GB}. The origin of {GB} is the same as that of {B} at the time of VIO initialization. The axes of {GB} are obtained by calculation at the time of VIO initialization, and its z-axis is aligned with Earth’s gravity.
- (3)
- GNSS Coordinate System {S}. The origin of {S} lies in the center of the GNSS equipment. The x-axis and y-axis point forward and to the right, respectively, following the right-hand rule.
- (4)
- Universal Transverse Mercator Coordinate System {US}. The {US} is a universal global coordinate system. Please refer to [51] for more details.
2.2. The Lever Arm Effect between the Vehicle and IMU Coordinates
2.3. Process Model and Monocular Visual Measurement Model
2.4. Kinematic Error Measurement Model for Vehicle
2.4.1. Measurements of Vehicle Relative Kinematic Error
2.4.2. Measurements of Vehicle Velocity and Angular Rate Error
2.5. Raw GNSS Error Measurement Model
3. Experiments and Results
3.1. Experimental Vehicle Platform and Real-World Datasets
3.2. Experimental Results
3.2.1. Observability and Consistency Comparison
3.2.2. Positioning Accuracy Comparison
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
Appendix B
Appendix C
Appendix D
References
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ACK-MSCKF | Tunable Parameter Configurations |
---|---|
ACK-MSCKF(1) | For Equations (A24) and (A31), |
ACK-MSCKF(2) | For Equations (A24) and (A31), |
ACK-MSCKF(3) | Same as Equations (A24) and (A31) |
ACK-MSCKF(4) | For Equation (A24), |
MAVIO | Tunable Parameter Configurations |
---|---|
MAVIO(1) | Same as Equations (A35) and (A40) |
MAVIO(2) | For Equation (A35), |
Dataset | Vehicle Driving State | Travel Duration (s) | Travel Distance (m) | Data Bulk | |||
---|---|---|---|---|---|---|---|
Vehicle CAN-Bus | Stereo Images | IMU | Ground Truth | ||||
VD01 | Straight | 54 | 109 | 8657 | 1615 | 10,808 | 10,818 |
VD02 | S-shaped | 60 | 122 | 9644 | 1820 | 12,146 | 12,186 |
VD03 | Circular | 99 | 162 | 15,631 | 2959 | 19,716 | 19,789 |
VD04 | Straight and Turning | 151 | 371 | 24,171 | 4532 | 30,164 | 30,244 |
VD05 | S-shaped and Straight and Turning | 170 | 400 | 27,135 | 5102 | 33,933 | 34,025 |
VD06 | Straight and Turning | 367 | 1085 | 58,463 | 11,014 | 73,169 | 73,386 |
Methods | VD01 | VD02 | VD03 | VD04 | VD05 | VD06 |
---|---|---|---|---|---|---|
MAVIO | 1.29 (a) | 1.29 (a,b) | 1.14 (b) | 1.42 (a) | 1.42 (b) | 1.63 (a) |
ACK-MSCKF | 1.21 (f) | 1.11 (f) | 0.98 (d,e,f) | 1.31 (f) | 1.21 (f) | 1.46 (f) |
S-MSCKF | 1.68 | 1.38 | 1.22 | 1.99 | 1.57 | 2.08 |
Methods | VD01 (m) | VD02 (m) | VD03 (m) | VD04 (m) | VD05 (m) | VD06 (m) |
---|---|---|---|---|---|---|
MAVIO | 1.31 (b) | 2.45 (b) | 0.78 (a) | 2.01 (b) | 3.16 (b) | 4.73 (b) |
MAVIO-GNSS | 1.22 | 2.27 | 0.79 | 1.67 | 3.59 | 3.28 |
ACK-MSCKF | 1.60 (d) | 2.57 (c) | 0.87 (d) | 2.46 (c,d) | 3.67 (d) | 3.80 (d) |
S-MSCKF | 9.78 | 2.31 | 2.44 | 14.83 | 4.25 | 21.76 |
Sub-Trajectory Length (m) | Relative Translation Error (m) | |||
---|---|---|---|---|
MAVIO | MAVIO-GNSS | ACK-MSCKF | S-MSCKF | |
1 | 0.08 (a, b) | 0.08 | 0.08 (c, d) | 0.22 |
5 | 0.23 (b) | 0.21 | 0.26 (c, d) | 0.93 |
10 | 0.40 (b) | 0.35 | 0.44 (c, d) | 1.71 |
20 | 0.70 (b) | 0.60 | 0.76 (d) | 3.00 |
50 | 1.51 (b) | 1.29 | 1.51 (d) | 5.57 |
100 | 2.79 (b) | 2.29 | 2.57 (d) | 8.33 |
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Ma, F.; Shi, J.; Wu, L.; Dai, K.; Zhong, S. Consistent Monocular Ackermann Visual–Inertial Odometry for Intelligent and Connected Vehicle Localization. Sensors 2020, 20, 5757. https://doi.org/10.3390/s20205757
Ma F, Shi J, Wu L, Dai K, Zhong S. Consistent Monocular Ackermann Visual–Inertial Odometry for Intelligent and Connected Vehicle Localization. Sensors. 2020; 20(20):5757. https://doi.org/10.3390/s20205757
Chicago/Turabian StyleMa, Fangwu, Jinzhu Shi, Liang Wu, Kai Dai, and Shouren Zhong. 2020. "Consistent Monocular Ackermann Visual–Inertial Odometry for Intelligent and Connected Vehicle Localization" Sensors 20, no. 20: 5757. https://doi.org/10.3390/s20205757