8 papers
\textbf{PLATO}: \emph{Preintegration Learning from Accurate Trajectory Observations} for Neural Inertial Odometry
Haoying Li, Qihang Liu, Yifan Peng +2
Neural inertial odometry has demonstrated strong potential for motion estimation in challenging environments, yet inertial-only preintegration remains sensitive to IMU bias and unc…
A High-accuracy Event-based Underwater SLAM System
Yifan Peng, Qihang Liu, Haoying Li +3
While event cameras offer immense potential for underwater SLAM, existing Time Surface (TS)-based methods prove highly unreliable when deployed underwater. Fluctuating camera veloc…
Provably Guaranteed Polytopic Uncertainty Quantification for SLAM
Guangyang Zeng, Yulong Gao, Yuan Shen +4
In safety-critical robotics applications, guaranteed and practical uncertainty quantification (UQ) in perception is vital. Many existing works either offer no formal containment gu…
Supervisory Measurement-Guided Noise Covariance Estimation: Discussing Forward and Reverse Differentiation
Haoying Li, Yifan Peng, Yuchi Wu +1
Reliable state estimation depends on accurately modeled noise covariances, which are difficult to determine in practice. This paper formulates the noise covariance estimation as a…
Supervisory Measurement-Guided Noise Covariance Estimation
Haoying Li, Yifan Peng, Xinghan Li +1
Reliable state estimation hinges on accurate specification of sensor noise covariances, which weigh heterogeneous measurements. In practice, these covariances are difficult to iden…
Distributed Invariant Kalman Filter for Object-level Multi-robot Pose SLAM
Haoying Li, Qingcheng Zeng, Haoran Li +2
Cooperative localization and target tracking are essential for multi-robot systems to implement high-level tasks. To this end, we propose a distributed invariant Kalman filter base…
Covariance Intersection-based Invariant Kalman Filtering(DInCIKF) for Distributed Pose Estimation
Haoying Li, Xinghan Li, Shuaiting Huang +2
This paper presents a novel approach to distributed pose estimation in the multi-agent system based on an invariant Kalman filter with covariance intersection. Our method models un…
Efficient Invariant Kalman Filter for Inertial-based Odometry with Large-sample Environmental Measurements
Xinghan Li, Haoying Li, Guangyang Zeng +4
A filter for inertial-based odometry is a recursive method used to estimate the pose from measurements of ego-motion and relative pose. Currently, there is no known filter that gua…