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. 2019 Nov 26;19(23):5178.
doi: 10.3390/s19235178.

A Novel Online Approach for Drift Covariance Estimation of Odometries Used in Intelligent Vehicle Localization

Mostafa Osman  1 , Ahmed Hussein  2 , Abdulla Al-Kaff  3 , Fernando García  3 , Dongpu Cao  4
Affiliations

A Novel Online Approach for Drift Covariance Estimation of Odometries Used in Intelligent Vehicle Localization

Mostafa Osman et al. Sensors (Basel). .

Erratum in

Abstract

Localization is the fundamental problem of intelligent vehicles. For a vehicle to autonomously operate, it first needs to locate itself in the environment. A lot of different odometries (visual, inertial, wheel encoders) have been introduced through the past few years for autonomous vehicle localization. However, such odometries suffers from drift due to their reliance on integration of sensor measurements. In this paper, the drift error in an odometry is modeled and a Drift Covariance Estimation (DCE) algorithm is introduced. The DCE algorithm estimates the covariance of an odometry using the readings of another on-board sensor which does not suffer from drift. To validate the proposed algorithm, several real-world experiments in different conditions as well as sequences from Oxford RobotCar Dataset and EU long-term driving dataset are used. The effect of the covariance estimation on three different fusion-based localization algorithms (EKF, UKF and EH-infinity) is studied in comparison with the use of constant covariance, which were calculated based on the true variance of the sensors being used. The obtained results show the efficacy of the estimation algorithm compared to constant covariances in terms of improving the accuracy of localization.

Keywords: adaptive filtering; covariance estimation; intelligent vehicles; localization; odometries drift errors; ros-based.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The covariance estimation algorithm operation structure with the corrective feedback localization from the localization algorithm to the odometry.
Figure 2
Figure 2
Visual demonstration of the three scenarios, where the green point is the start point and the blue curve is the path drawn using the LiDAR odometry. The Figure is reproduced from [19] (© 2018 IEEE).
Figure 3
Figure 3
Visual demonstration of one of Scenario I experiments using EKF for localization.
Figure 4
Figure 4
Visual demonstration of one of Scenario II experiments using UKF for localization.
Figure 5
Figure 5
Visual demonstration of one of Scenario II experiments using UKF for localization.
Figure 6
Figure 6
The EU dataset results of the DCE-UKF, LOAM, and GPS.
See this image and copyright information in PMC

References

    1. Urmson C., Anhalt J., Bagnell D., Baker C., Bittner R., Clark M., Dolan J., Duggins D., Galatali T., Geyer C., et al. Autonomous driving in urban environments: Boss and the urban challenge. J. Field Robot. 2008;25:425–466. doi: 10.1002/rob.20255. - DOI
    1. Kelly J., Sukhatme G.S. Visual-inertial sensor fusion: Localization, mapping and sensor-to-sensor self-calibration. Int. J. Robot. Res. 2011;30:56–79. doi: 10.1177/0278364910382802. - DOI
    1. Luo R.C., Chang C.C. Multisensor Fusion and Integration: A Review on Approaches and Its Applications in Mechatronics. IEEE Trans. Ind. Inform. 2012;8:49–60. doi: 10.1109/TII.2011.2173942. - DOI
    1. Duan P., Tian G., Wu H. A multi-sensor-based mobile robot localization framework; Proceedings of the 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014); Bali, Indonesia. 5–10 December 2014; pp. 642–647.
    1. Magrin C.E.S., Todt E. Hierarchical Sensor Fusion Method Based on Fingerprint kNN and Fuzzy Features Weighting for Indoor Localization of a Mobile Robot Platform; Proceedings of the 2016 XIII Latin American Robotics Symposium and IV Brazilian Robotics Symposium (LARS/SBR); Recife, Brazil. 8–12 October 2016; pp. 305–310.

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