在不同复杂环境中，各种传感器的性能会有所不同。它们各自具有优势和劣势，因此融合多模态数据提供了一种解决方案，可以克服各个传感器单独使用时的限制[90]–[92]。此外，许多讨论的传感器已经广泛应用于自动驾驶领域，因此开发利用车载所有硬件和感知模态的算法是有意义的。雷达传感器存在一些问题，例如幽灵物体、低分辨率、多径反射、运动畸变和饱和等。相机也有自己的问题，比如对光照和天气条件敏感。激光雷达在一定程度上也会受到恶劣天气条件和运动畸变的影响。典型的IMU具有噪声且容易漂移，最后，轮速编码器容易受到车轮滑动的影响。除了与这些传感器相关的各种弱点之外，单模态里程计算法通常也存在其固有的问题。例如，单目视觉里程计可以估计运动的尺度，而立体视觉里程计对于校准、校正、视差和三角测量的质量非常敏感，当深度远大于立体对的基线时，立体视觉也会退化成单目视觉。基于扫描雷达的雷达里程计通常具有较低的频率。基于激光雷达的里程计通常在计算上要求很高，其基于扫描匹配的方法需要良好的初始化。尽管使用传感器融合在里程计中具有预期的优点，但我们强调雷达里程计的出版工作中实际应用传感器融合技术的比例略低于预期（图2d），我们猜测这背后的原因可能是传感器融合硬件和方法的标准化缺乏，此外，单模态方法的成熟性和稳健性使得证明多模态方法的必要性越来越困难。接下来，我们对雷达里程计文献中发现的传感器融合方法进行简要概述。在这里，我们限制讨论使用其他传感器（例如相机、IMU）数据应在运行时可用的方法，只有使用其他传感器数据进行训练、测试、校准或作为地面真实数据源的方法不被视为融合方法。

最常见的雷达里程计传感器配置是雷达和IMU的组合；典型IMU的高采样率与雷达的采样率相辅相成，雷达定期纠正IMU的不良漂移。这种传感器组合在无人机应用中取得了巨大成功，其中卡尔曼滤波器或其变体用于融合两个传感器的数据[93]–[98]。类似地，Almalioglu等人[63]和Araujo等人[69]的工作基于使用卡尔曼滤波器的变体来融合雷达和IMU数据。使用卡尔曼滤波器融合的一个优点是相对容易扩展到包括更多传感器，例如Ort等人[85]将地面探测雷达、轮速编码器和IMU的数据融合在一起。Holder等人[66]融合了经过预处理的雷达数据、陀螺仪、轮速编码器和方向盘角度。最后，Liang等人[68]在一个可扩展的框架中融合了雷达、激光雷达、相机、GNSS和IMU的数据。

另一方面，Lu等人[84]采用深度学习，并提出了一种自我和交叉注意力的混合机制，用于融合雷达和IMU的数据，他们声称他们的方法在模型内部优于简单的特征级联，并且可以轻松扩展到包括更多传感器。Fritsche和Wagner[14]使用手工制定的启发式方法将雷达和激光雷达的探测结果。

8. 雷达里程计与机器学习

机器学习技术已广泛应用于各种机器人感知任务，并在自动驾驶汽车和机器人领域取得了许多成功案例[99]，[100]。学习技术利用现代硬件的计算能力来处理大量数据，从而实现无需建模的方法。尽管如此，我们发现使用任何学习技术的出版物数量比预期的少（图2c）。这可能与学习方法的泛化困难有关，这是机器学习领域普遍存在的问题，但在里程计和SLAM领域尤为难以解决。以下是在雷达里程计文献中发现的一些学习方法的简要概述。

Barnes等人[82]和Weston等人[83]的工作是基于训练一个CNN来预测可以用于滤除嘈杂雷达扫描的掩码。他们在训练阶段使用视觉里程计作为地面真实位姿的来源。Aldera等人[50]也使用了U-Net风格的CNN来生成过滤掉雷达噪声和伪影的掩码，并且他们使用视觉里程计作为地面真实数据。Barnes和Posner[42]使用U-Net风格的CNN来预测关键点、得分和描述符。Burnett等人[45]也训练了一个U-Net CNN来预测关键点、得分和描述符，但他们使用无监督学习来训练模型。Aldera等人[51]使用SVM来对关联地标的相容性矩阵的特征向量进行分类，以区分好的和坏的估计。Araujo等人[69]仅将CNN用作预处理步骤，以对雷达数据进行去噪。Zhu等人[54]开发了一个神经网络模型，用于处理雷达点云并生成逐点的权重和偏移量，可用于算法的其他阶段进行运动估计。Almalioglu等人[63]使用RNN作为运动模型，以便从先前姿态的信息中受益，并更好地捕捉运动的动态特性。在Lu等人[84]中，使用了各种学习技术，从处理雷达数据的CNN，到处理IMU数据的RNN，再到混合注意机制，用于将两个流融合在一起，然后使用LSTM在将输出传递给全连接网络进行姿态回归之前从先前姿态中获得益处。最后，Alhashimi等人[58]提出了一种基于可学习参数的雷达滤波器。

因此，看起来在学习技术中，扫描雷达比汽车雷达更常见。这可能是因为扫描雷达产生更丰富的数据，深度学习模型对此非常吸引。此外，我们注意到CNN是雷达里程计中最流行的学习技术，这是因为扫描雷达的扫描与视觉图像相似，因此在任务如语义分割和物体检测中使用CNN非常常见。

9. 讨论、挑战与未来建议

实际情况下，雷达传感器不太可能完全取代相机和激光雷达在感知领域的作用。事实上，目前可用的雷达在物体识别、采样率或信噪比方面无法与相机/激光雷达相竞争。我们预期雷达将在自主平台的传感器组合中继续发挥重要的辅助作用。汽车雷达已经在自动驾驶汽车市场得到广泛应用，其成本相对于激光雷达仍然较低，并且在恶劣天气条件下更加可靠。以下是阻碍雷达里程计进展以及未来建议的一些挑战。

1. 雷达几乎总是被提出作为解决恶劣天气问题以及激光雷达和相机在此类情况下性能下降的解决方案；然而，两种最具挑战性的条件，雾和尘土，却是当前可用数据集中最少的条件。对添加合成雾的研究取得了很大进展（例如[101]），但在雾和尘土条件下进行真实驾驶数据收集仍然更为理想。然而，我们也认识到在这些条件下预先预测和记录数据的难度，尤其是雾的情况。
2. Navtech的热门扫描雷达被认为采样率较低，只有4Hz。考虑到大多数公开可用数据集是在相对较低的行驶速度下记录的（如图7所示），这意味着我们的雷达算法未经受过中高速行驶的测试。这是一个问题，因为这可能意味着我们对雷达里程计算法的当前评估最多只是乐观的。
3. 雷达感知研究，特别是雷达里程计方面，缺少类似KITTI数据集和排行榜的公共参考点；一个让研究人员测试和对比他们的工作的共同参考标准。虽然牛津雷达机器人车数据集在一定程度上填补了这一角色，但牛津数据集在季节多样性和行驶速度方面非常有限。此外，它缺少维护的排行榜，而Boreas数据集试图解决这个问题。
4. 最受欢迎的雷达数据集是使用扫描雷达收集的，目前没有公开的基于汽车雷达的数据集引起足够的关注，被视为“雷达里程计基准”，相反，基于汽车雷达的研究的常见做法是让研究人员收集和测试自己未发表的数据，这使得很难比较和评估针对汽车雷达开发的不同里程计方法。
5. 最受欢迎的评估指标，平均平移误差和平均旋转误差（见子节IV-C），是为KITTI数据集量身定制的；(100, 200, 300, 400, 500, 600, 700和800)米的距离范围不一定适用于其他数据集。可以将其推广，以适应更长或更短距离的轨迹。

10. 结论

基于雷达的里程计是在恶劣环境中估计机器人位置和方向变化的最佳解决方案之一。雷达在机器人领域已经得到广泛应用，并拥有许多优点，使其成为传感器组合中的重要组成部分；此外，雷达技术日益改进，价格更便宜，体积更小。本文对雷达里程计领域的相关工作进行了调研，重点关注了用于自主地面车辆或机器人的雷达里程计算法。调查了雷达里程计研究的当前趋势，包括方法、传感器类型以及传感器融合和机器学习技术的应用。文章还概述了雷达传感器的工作原理，雷达里程计的标准评估指标以及可用于雷达里程计研究的公开数据集。此外，对文献中发现的雷达里程计方法进行了系统分类。虽然基于雷达的状态估计技术并不新鲜，但是雷达传感器技术的最新进展和对自主性、安全性和全天候功能的增加期望为这一领域留下了广阔的发展空间，还有更多的工作需要在这个领域进行。

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