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 Cramér–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.

中文翻译：

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里程表偏差补偿的基于EKF的雷达SLAM的实验结果和后Cramér-Rao界分析

与光学传感器不同，安装在移动车辆上的雷达会返回对应于不稳定散射点的时变检测，而光学传感器会产生相对稳定的检测。我们提出了一种有效的基于扩展卡尔曼滤波的同时定位和制图（EKF-SLAM）雷达算法，它利用聚类和筛选时变检测的新技术。任何里程表中固有的速度偏差和偏航率偏差也可以使用相同的EKF进行估算和补偿。对于理论性能评估，推导了具有里程表偏差估计的SLAM的后Cramér-Rao界（PCRB），并将其与有无偏差估计的均方根误差（RMSE）进行比较。仿真结果表明，带有偏差估计的SLAM的RMSE最接近PCRB。该算法也得到了现场数据的实验验证。与从差分全球定位系统获得的地面真实轨迹的比较表明，即使在较大的里程表偏差下，该算法也能产生准确的轨迹估计。同时，验证了所提算法的实时性。