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cs.RO2026
Millimeter Wave Radar: From Synthetic Aperture to Probabilistic Mapping
Jui-Te Huang, Ruoyang Xu, Michael Kaess
Robust probabilistic mapping is essential for autonomous robotic systems operating in challenging environments. While traditional sensors fail in adverse conditions such as smoke a…
cs.RO2026
UNRIO: Uncertainty-Aware Velocity Learning for Radar-Inertial Odometry
Jui-Te Huang, Tianshu Huang, Tinashu Huang +2
mmWave radars are robust to darkness and occlusions such as dust and smoke, and can directly constrain ego-velocity from a single frame via Doppler measurements, making them attrac…
cs.RO2024
BEVLoc: Cross-View Localization and Matching via Birds-Eye-View Synthesis
Christopher Klammer, Michael Kaess
Ground to aerial matching is a crucial and challenging task in outdoor robotics, particularly when GPS is absent or unreliable. Structures like buildings or large dense forests cre…