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首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >Experimental Results and Posterior Cramér–Rao Bound Analysis of EKF-Based Radar SLAM With Odometer Bias Compensation
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Experimental Results and Posterior Cramér–Rao Bound Analysis of EKF-Based Radar SLAM With Odometer Bias Compensation

机译：高压仪偏置补偿的实验结果和后Cramér-Rao响应分析

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摘要

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.
机译：与光学传感器不同，安装在移动车辆上的雷达返回对应于不稳定散射点的时变检测，其产生相对稳定的检测。我们为雷达提供了一种高效的扩展Kalman滤波器的同时定位和映射（EKF-SLAM）算法，利用集群的新技术和筛选时变检测。速度偏置和横摆率偏置，这些偏差也是估计和补偿的任何内径计中固有的偏差。使用相同的EKF来补偿。对于理论性能评估，衍生利用里程表偏差估计的后克拉姆-RAO结合（PCRB），并与具有和无偏压估计的根均平方误差（RMSE）进行比较。仿真结果表明，具有偏置估计的SLAM的RMSE是最接近PCRB的。所提出的算法也通过实验验证现场数据。与差分全球定位系统中获得的地面真理轨迹的比较表明，即使在大的里程表偏压下，所提出的算法也能产生精确的轨迹估计。此外，验证了所提出的算法的实时能力。

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