We promote in this paper the processing of radar data in the frequency domain to achieve higher robustness against noise and structural errors, especially in comparison to feature-based methods. This holds also for high dynamics in the scene, i.e., ego-motion of the vehicle with the sensor plus the presence of an unknown number of other moving objects. In addition to the high robustness, the processing in the frequency domain has the so far neglected advantage that the underlying correlation based methods used for, e.g., registration, provide information about all moving structures in the scene. A typical automotive application case is overtaking maneuvers, which in the context of autonomous racing are used here as a motivating example. Initial experiments and results with Fourier SOFT in 2D (FS2D) are presented that use the Boreas dataset to demonstrate radar-only-odometry, i.e., radar-odometry without sensor-fusion, to support our arguments.

翻译：本文提出在频域处理雷达数据，以增强对噪声和结构误差的鲁棒性，尤其相较于基于特征的方法更具优势。该方法同样适用于场景中的高动态情况，即搭载传感器的车辆自运动及场景中未知数量的其他运动物体。除高鲁棒性外，频域处理还具有迄今被忽视的优点：用于配准等任务的底层相关方法可提供场景中所有运动结构的信息。典型汽车应用场景为超车操作，本文以自主赛车环境作为激励案例。通过采用Boreas数据集，我们展示了基于二维傅里叶软变换（FS2D）的雷达单模态里程计（即无传感器融合的雷达里程计）初步实验与结果，以支持本文论点。