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arXiv · 2309.08731

Pointing the Way: Refining Radar-Lidar Localization Using Learned ICP Weights

Daniil Lisus·Johann Laconte·Keenan Burnett·Ziyu Zhang·Timothy D. Barfoot
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Abstract

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.

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Daniil Lisus, Johann Laconte, Keenan Burnett, Ziyu Zhang, Timothy D. Barfoot. 2023-09-15. Pointing the Way: Refining Radar-Lidar Localization Using Learned ICP Weights. https://arxiv.org/abs/2309.08731

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