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

arXiv:2309.08731v2 Announce Type: replace-cross
Abstract: This paper presents a novel deep-learning-based approach to improve localizing radar measurements against lidar maps. Although the state of the art for localization is matching lidar data to lidar maps, radar has been considered as a promising alternative. This is largely due to radar being more resilient against adverse weather such as precipitation and heavy fog. To make use of existing high-quality lidar maps, while maintaining performance in adverse weather, it is of interest to …

abstract art arxiv cs.lg cs.ro data lidar localization maps novel paper radar state state of the art the way type