5 papers
DINS-IO: Learned Inertial Odometry via Differentiable INS Consistency
Hao Qiao, Yan Wang, Jian Kuang +1
The training of learned inertial odometry depends on dense, high-precision position ground truth from motion capture, visual-inertial odometry or SLAM, which is costly and hard to…
MoE-Based Learned Inertial Odometry for Bicycle Localization
Hao Qiao, Yan Wang, Shuo Yang +3
GNSS suffers from multipath errors in urban canyons, making reliable bicycle localization difficult. Hand-crafted inertial alternatives, such as cycling dead reckoning and nonholon…
DogLegs: Robust Proprioceptive State Estimation for Legged Robots Using Multiple Leg-Mounted IMUs
Yibin Wu, Jian Kuang, Shahram Khorshidi +4
Robust and accurate proprioceptive state estimation of the main body is crucial for legged robots to execute tasks in extreme environments where exteroceptive sensors, such as LiDA…
MSCEKF-MIO: Magnetic-Inertial Odometry Based on Multi-State Constraint Extended Kalman Filter
Jiazhu Li, Jian Kuang, Xiaoji Niu
To overcome the limitation of existing indoor odometry technologies which often cannot simultaneously meet requirements for accuracy cost-effectiveness, and robustness-this paper p…
Wheel-GINS: A GNSS/INS Integrated Navigation System with a Wheel-mounted IMU
Yibin Wu, Jian Kuang, Xiaoji Niu +3
A long-term accurate and robust localization system is essential for mobile robots to operate efficiently outdoors. Recent studies have shown the significant advantages of the whee…