Ni Li — accepted AI-conference papers

2025

Monocular Visual-Inertial Odometry Based on Local Maximum A Posteriori Estimation

Bipeng Ye, Guanghong Gong, Ni Li, Yunbo Gao, Tiantian Zhang, Guoqing Hou

RA-L 2025

Monocular visual-inertial odometry can effectively solve the problem of unobservable metric scale in monocular odometry. However, due to differences in sensor observation noise and redundant constraints, the positioning accuracy of a general monocular visual-inertial odometer may decline compared wi

Cited by 2 Source ↗Scholar ↗

2023

CCVO: Cascaded CNNs for Fast Monocular Visual Odometry Towards the Dynamic Environment

Tiantian Zhang, Ni Li, Guanghong Gong, Chaopin Yang, Guoqing Hou, Xin Lin

RA-L 2023

For AR applications, the present monocular VO methods can be further improved to provide more real-time and accurate self-localization in a dynamic environment with motion disturbance. This letter proposes the CCVO (Cascaded CNNs for Visual Odometry) which is a monocular VO approach to realize end-t

Cited by 10 Source ↗Scholar ↗