Conference item
RSS-Net: weakly-supervised multi-class semantic segmentation with FMCW radar
- Abstract:
- This paper presents an efficient annotation procedure and an application thereof to end-to-end, rich semantic segmentation of the sensed environment using Frequency-Modulated Continuous-Wave 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 stateof-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 posechain 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.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
-
(Preview, Accepted manuscript, 2.8MB, Terms of use)
-
- Publisher copy:
- 10.1109/IV47402.2020.9304674
Authors
- Publisher:
- IEEE
- Journal:
- Proceedings of the 2020 IEEE Intelligent Vehicles Symposium (IV2020) More from this journal
- Issue:
- 2020
- Pages:
- 431-436
- Publication date:
- 2021-01-08
- Acceptance date:
- 2020-04-01
- Event title:
- IEEE Intelligent Vehicles Symposium (IV)
- Event location:
- Las Vegas, Nevada, United States
- Event website:
- https://2020.ieee-iv.org/
- Event start date:
- 2020-10-19
- Event end date:
- 2020-11-13
- DOI:
- EISSN:
-
2642-7214
- ISSN:
-
1931-0587
- EISBN:
- 978-1-7281-6673-5
- ISBN:
- 978-1-7281-6674-2
- Language:
-
English
- Keywords:
- Pubs id:
-
1098455
- Local pid:
-
pubs:1098455
- Deposit date:
-
2020-04-03
Terms of use
- Copyright holder:
- IEEE
- Copyright date:
- 2021
- Rights statement:
- © IEEE 2021.
- Notes:
- This paper was presented at the 2020 IEEE Intelligent Vehicles Symposium (IV), 19th October - 13th November 2020, Las Vegas, Nevada. This is the accepted manuscript version of the article. The final version is available from IEEE at: https://doi.org/10.1109/IV47402.2020.9304674
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