道を示す：学習済みICP重みによるレーダー・ライダー位置推定の改良
Pointing the Way: Refining Radar-Lidar Localization Using Learned ICP Weights
レーダー点群に学習ベースの重み付け前処理を施し、ICPによるライダーマップへの位置合わせ精度を向上させる手法を提案。微分可能なICPライブラリも公開。
著者: Daniil Lisus, Johann Laconte, Keenan Burnett, Ziyu Zhang, Timothy D. Barfoot
分類: cs.RO, cs.LG
原文アブストラクト
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.
