This paper presents an efficient annotation procedure and an application
thereof to end-to-end, rich semantic segmentation of the sensed environment
using FMCW scanning radar. We advocate radar over the traditional sensors used
for this task as it operates at longer ranges and is substantially more robust
to adverse weather and illumination conditions. We avoid laborious manual
labelling by exploiting the largest radar-focused urban autonomy dataset
collected to date, correlating radar scans with RGB cameras and LiDAR sensors,
for which semantic segmentation is an already consolidated procedure. The
training procedure leverages a state-of-the-art natural image segmentation
system which is publicly available and as such, in contrast to previous
approaches, allows for the production of copious labels for the radar stream by
incorporating four camera and two LiDAR streams. Additionally, the losses are
computed taking into account labels to the radar sensor horizon by accumulating
LiDAR returns along a pose-chain ahead and behind of the current vehicle
position. Finally, we present the network with multi-channel radar scan inputs
in order to deal with ephemeral and dynamic scene objects.

本文介绍了一种有效的注释过程以及其应用，旨在利用 FMCW 扫描雷达对感知环境进行端到端的丰富语义分割。通过利用迄今为止收集的最大雷达集中的城市自治数据集，我们避免了繁琐的手动标注，并将雷达扫描与 RGB 相机和 LiDAR 传感器相关联，在此过程中，语义分割已经是一个已经巩固的程序。最后，我们使用多通道雷达扫描输入呈现网络，以处理短暂的和动态的场景对象。