3 papers
cs.RO2025
Breaking the Static Assumption: A Dynamic-Aware LIO Framework Via Spatio-Temporal Normal Analysis
Chen Zhiqiang, Le Gentil Cedric, Lin Fuling +5
This paper addresses the challenge of Lidar-Inertial Odometry (LIO) in dynamic environments, where conventional methods often fail due to their static-world assumptions. Traditiona…
cs.RO2025
DEIO: Deep Event Inertial Odometry
Weipeng Guan, Fuling Lin, Peiyu Chen +1
Event cameras show great potential for visual odometry (VO) in handling challenging situations, such as fast motion and high dynamic range. Despite this promise, the sparse and mot…
cs.CV2025
SuperEIO: Self-Supervised Event Feature Learning for Event Inertial Odometry
Peiyu Chen, Fuling Lin, Weipeng Guan +1
Event cameras asynchronously output low-latency event streams, promising for state estimation in high-speed motion and challenging lighting conditions. As opposed to frame-based ca…