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This paper presents an efficient annotation procedure and an applicationthereof to end-to-end, rich semantic segmentation of the sensed environmentusing FMCW scanning radar. We advocate radar over the traditional sensors usedfor this task as it operates at longer ranges and is substantially more robustto adverse weather and illumination conditions. We avoid laborious manuallabelling by exploiting the largest radar-focused urban autonomy datasetcollected to date, correlating radar scans with RGB cameras and LiDAR sensors,for which semantic segmentation is an already consolidated procedure. Thetraining procedure leverages a state-of-the-art natural image segmentationsystem which is publicly available and as such, in contrast to previousapproaches, allows for the production of copious labels for the radar stream byincorporating four camera and two LiDAR streams. Additionally, the losses arecomputed taking into account labels to the radar sensor horizon by accumulatingLiDAR returns along a pose-chain ahead and behind of the current vehicleposition. Finally, we present the network with multi-channel radar scan inputsin order to deal with ephemeral and dynamic scene objects.

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