Experimental Results and Posterior Cramer-Rao Bound Analysis of EKF-Based Radar SLAM With Odometer Bias Compensation

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Radar mounted on a moving vehicle returns time-varying detections corresponding to unstable scattering points, unlike optical sensors, which produce relatively stable detections. We present an efficient extended Kalman filter-based simultaneous localization and mapping (EKF-SLAM) algorithm for radar, utilizing new techniques of clustering and sifting the time-varying detections. Velocity bias and yaw rate bias, which are inherent in any odometer are also estimated and compensated using the same EKF. For theoretical performance evaluation, the posterior Cramer-Rao bound (PCRB) for SLAM with odometer bias estimation is derived and compared with the root-mean-squared errors (RMSEs), with and without bias estimation. The simulation results show that the RMSE for SLAM with bias estimation is the closest to the PCRB. The proposed algorithm is also verified experimentally with field data. The comparison with the ground truth trajectory obtained from a differential global positioning system shows that the proposed algorithm yields an accurate trajectory estimate, even under large odometer bias. Also, the real-time capability of the proposed algorithm is verified.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2021-02
Language
English
Article Type
Article
Citation

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, v.57, no.1, pp.310 - 324

ISSN
0018-9251
DOI
10.1109/TAES.2020.3016873
URI
http://hdl.handle.net/10203/282297
Appears in Collection
EE-Journal Papers(저널논문)
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