5 papers
FoMo: A Multi-Season Dataset for Robot Navigation in Forêt Montmorency
MatÄj Boxan, Gabriel Jeanson, Alexander Krawciw +5
The Forêt Montmorency (FoMo) dataset is a comprehensive multi-season data collection, recorded over the span of one year in a boreal forest. Featuring a unique combination of on-…
DRO: Doppler-Aware Direct Radar Odometry
Cedric Le Gentil, Leonardo Brizi, Daniil Lisus +3
A renaissance in radar-based sensing for mobile robotic applications is underway. Compared to cameras or lidars, millimetre-wave radars have the ability to `see' through thin walls…
RaSCL: Radar to Satellite Crossview Localization
Blerim Abdullai, Tony Wang, Xinyuan Qiao +2
GNSS is unreliable, inaccurate, and insufficient in many real-time autonomous field applications. In this work, we present a GNSS-free global localization solution that contains a…
Radar Teach and Repeat: Architecture and Initial Field Testing
Xinyuan Qiao, Alexander Krawciw, Sven Lilge +1
Frequency-modulated continuous-wave (FMCW) scanning radar has emerged as an alternative to spinning LiDAR for state estimation on mobile robots. Radar's longer wavelength is less a…
FoMo: A Proposal for a Multi-Season Dataset for Robot Navigation in Forêt Montmorency
MatÄj Boxan, Alexander Krawciw, Effie Daum +4
In this paper, we propose the FoMo (Forêt Montmorency) dataset: a comprehensive, multi-season data collection. Located in the Montmorency Forest, Quebec, Canada, our dataset will…