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。

RSS-Net: 基于 FMCW 雷达的弱监督多类语义分割

RSS-Net: Weakly-Supervised Multi-Class Semantic Segmentation with FMCW  Radar

This paper details an application which yields significant improvements to
the adeptness of place recognition with Frequency-Modulated Continuous-Wave
radar - a commercially promising sensor poised for exploitation in mobile
autonomy. We show how a rotationally-invariant metric embedding for radar scans
can be integrated into sequence-based trajectory matching systems typically
applied to videos taken by visual sensors. Due to the complete horizontal field
of view inherent to the radar scan formation process, we show how this
off-the-shelf sequence-based trajectory matching system can be manipulated to
detect place matches when the vehicle is travelling down a previously visited
stretch of road in the opposite direction. We demonstrate the efficacy of the
approach on 26 km of challenging urban driving taken from the largest
radar-focused urban autonomy dataset released to date -- showing a boost of 30%
in recall at high levels of precision over a nearest neighbour approach.

该研究论文详细介绍了利用频率调制连续波雷达进行地点识别的应用程序，通过将雷达扫描的旋转不变度量嵌入到视频传感器拍摄的序列轨迹匹配系统中，实现了对车辆行驶方向相反的先前行驶路段的地点匹配检测，并在来自至今最大的面向雷达自动驾驶的城市数据集的 26 公里具有挑战性的都市驾驶中展示了该方法的有效性，其召回率比最近邻方法提高了 30% 以上，精度高。