Within the autonomous driving community, millimeter-wave frequency-modulated continuous-wave (FMCW) radars are not used to their fullest potential. Classical, hand-designed target detection algorithms are applied in the signal processing chain and the rich contextual information is discarded. This early discarding of information limits what can be applied in algorithms further downstream. In contrast with object detection in camera images, radar has thus been unable to benefit fully from data-driven methods. This work seeks to bridge this gap by providing the community with a diverse, minimally processed FMCW radar dataset that is not only RGB-D (color and depth) aligned but also synchronized with inertial measurement unit (IMU) measurements in the presence of ego-motion. Moreover, having time-synchronized measurements allow for verification, automated or assisted labelling of the radar data, and opens the door for novel methods of fusing the data from a variety of sensors. We present a system that could be built with accessible, off-the-shelf components within a $1000 budget and an accompanying dataset consisting of diverse scenes spanning indoor, urban and highway driving. Finally, we demonstrated the ability to go beyond classical radar object detection with our dataset with a classification accuracy of 85.1% using the low-level radar signals captured by our system, supporting our argument that there is value in retaining the information discarded by current radar pipelines.

中文翻译：

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RaDICaL：具有低级 FMCW 雷达信号的同步 FMCW 雷达、深度、IMU 和 RGB 相机数据集

在自动驾驶社区内，毫米波调频连续波 (FMCW) 雷达并未发挥其最大潜力。经典的、手工设计的目标检测算法被应用在信号处理链中，并丢弃了丰富的上下文信息。这种信息的早期丢弃限制了可以应用于更下游算法的内容。与相机图像中的物体检测相比，雷达因此无法从数据驱动的方法中充分受益。这项工作旨在通过向社区提供多样化、最少处理的 FMCW 雷达数据集来弥合这一差距，该数据集不仅与 RGB-D（颜色和深度）对齐，而且在存在自我的情况下与惯性测量单元 (IMU) 测量同步。运动。此外，具有时间同步的测量允许验证，自动或辅助标记雷达数据，并为融合来自各种传感器的数据的新方法打开了大门。我们提出了一个系统，该系统可以在 1000 美元的预算内使用可访问的现成组件和随附的数据集来构建，该数据集由跨越室内、城市和高速公路驾驶的不同场景组成。最后，我们展示了使用我们系统捕获的低级雷达信号以 85.1% 的分类精度超越经典雷达目标检测的能力，支持我们的论点，即保留当前雷达丢弃的信息是有价值的管道。1000 美元预算内的现成组件和随附的数据集，包括跨越室内、城市和高速公路驾驶的不同场景。最后，我们展示了使用我们系统捕获的低级雷达信号以 85.1% 的分类精度超越经典雷达目标检测的能力，支持我们的论点，即保留当前雷达丢弃的信息是有价值的管道。1000 美元预算内的现成组件和随附的数据集，包括跨越室内、城市和高速公路驾驶的不同场景。最后，我们展示了使用我们系统捕获的低级雷达信号以 85.1% 的分类精度超越经典雷达目标检测的能力，支持我们的论点，即保留当前雷达丢弃的信息是有价值的管道。