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

• URL: http://arxiv.org/abs/2309.08731v3
• Date: Wed, 21 Aug 2024 22:22:09 GMT
• Title: Pointing the Way: Refining Radar-Lidar Localization Using Learned ICP Weights
• Authors: Daniil Lisus, Johann Laconte, Keenan Burnett, Ziyu Zhang, Timothy D. Barfoot,
• Abstract summary: We build 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.
• Score: 10.613476233222347
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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