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This paper presents a novel deep-learning-based approach to improvelocalizing radar measurements against lidar maps. Although the state of the artfor localization is matching lidar data to lidar maps, radar has beenconsidered as a promising alternative, as it is potentially more resilientagainst adverse weather such as precipitation and heavy fog. To make use ofexisting high-quality lidar maps, while maintaining performance in adverseweather, matching radar data to lidar maps is of interest. However, owing inpart to the unique artefacts present in radar measurements, radar-lidarlocalization has struggled to achieve comparable performance to lidar-lidarsystems, preventing it from being viable for autonomous driving. This workbuilds on an ICP-based radar-lidar localization system by including a learnedpreprocessing step that weights radar points based on high-level scaninformation. Combining a proven analytical approach with a learned weightreduces localization errors in radar-lidar ICP results run on real-worldautonomous driving data by up to 54.94% in translation and 68.39% in rotation,while maintaining interpretability and robustness.

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