全天候マッピングと自己位置推定においてレーダーはライダーを置き換えられるか？
Are We Ready for Radar to Replace Lidar in All-Weather Mapping and Localization?
Boreasデータセットを用いてレーダーのみ・ライダーのみ・レーダーとライダーのクロスモーダルな自己位置推定を季節や天候を変えて比較し、吹雪でもライダーが最高精度だった一方、レーダーは小さい地図で競争力のある精度を達成した。
著者: Keenan Burnett, Yuchen Wu, David J. Yoon, Angela P. Schoellig, Timothy D. Barfoot
分類: cs.RO
原文アブストラクト
We present an extensive comparison between three topometric localization systems: radar-only, lidar-only, and a cross-modal radar-to-lidar system across varying seasonal and weather conditions using the Boreas dataset. Contrary to our expectations, our experiments showed that our lidar-only pipeline achieved the best localization accuracy even during a snowstorm. Our results seem to suggest that the sensitivity of lidar localization to moderate precipitation has been exaggerated in prior works. However, our radar-only pipeline was able to achieve competitive accuracy with a much smaller map. Furthermore, radar localization and radar sensors still have room to improve and may yet prove valuable in extreme weather or as a redundant backup system. Code for this project can be found at: https://github.com/utiasASRL/vtr3
