Pointing the Way: Refining Radar-Lidar Localization Using Learned ICP Weights
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866908380382101504 |
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| author | Lisus, Daniil Laconte, Johann Burnett, Keenan Zhang, Ziyu Barfoot, Timothy D. |
| author_facet | Lisus, Daniil Laconte, Johann Burnett, Keenan Zhang, Ziyu Barfoot, Timothy D. |
| contents | This paper presents a novel deep-learning-based approach to improve localizing radar measurements against lidar maps. This radar-lidar localization leverages the benefits of both sensors; radar is resilient against adverse weather, while lidar produces high-quality maps in clear conditions. However, owing in part to the unique artefacts present in radar measurements, radar-lidar localization has struggled to achieve comparable performance to lidar-lidar systems, preventing it from being viable for autonomous driving. This work builds on ICP-based radar-lidar localization by including a learned preprocessing step that weights radar points based on high-level scan information. To train the weight-generating network, we present a novel, stand-alone, open-source differentiable ICP library. The learned weights facilitate ICP by filtering out harmful radar points related to artefacts, noise, and even vehicles on the road. Combining an analytical approach with a learned weight reduces overall localization errors and improves convergence in radar-lidar ICP results run on real-world autonomous driving data. Our code base is publicly available to facilitate reproducibility and extensions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_08731 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Pointing the Way: Refining Radar-Lidar Localization Using Learned ICP Weights Lisus, Daniil Laconte, Johann Burnett, Keenan Zhang, Ziyu Barfoot, Timothy D. Robotics Machine Learning This paper presents a novel deep-learning-based approach to improve localizing radar measurements against lidar maps. This radar-lidar localization leverages the benefits of both sensors; radar is resilient against adverse weather, while lidar produces high-quality maps in clear conditions. However, owing in part to the unique artefacts present in radar measurements, radar-lidar localization has struggled to achieve comparable performance to lidar-lidar systems, preventing it from being viable for autonomous driving. This work builds on ICP-based radar-lidar localization by including a learned preprocessing step that weights radar points based on high-level scan information. To train the weight-generating network, we present a novel, stand-alone, open-source differentiable ICP library. The learned weights facilitate ICP by filtering out harmful radar points related to artefacts, noise, and even vehicles on the road. Combining an analytical approach with a learned weight reduces overall localization errors and improves convergence in radar-lidar ICP results run on real-world autonomous driving data. Our code base is publicly available to facilitate reproducibility and extensions. |
| title | Pointing the Way: Refining Radar-Lidar Localization Using Learned ICP Weights |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2309.08731 |