9 papers
Natural Gradient Bayesian Filtering: Geometry-Aware Filter for Dynamical Systems
Chang Liu, Wenhan Cao, Zeju Sun +8
Bayesian filtering is a cornerstone of state estimation in complex systems such as aerospace systems, yet exact solutions are available only for linear Gaussian models. In practice…
Natural Gradient Gaussian Approximation Filter on Lie Groups for Robot State Estimation
Tianyi Zhang, Wenhan Cao, Chang Liu +2
Accurate state estimation for robotic systems evolving on Lie group manifolds, such as legged robots, is a prerequisite for achieving agile control. However, this task is challenge…
One Filters All: A Generalist Filter for State Estimation
Shiqi Liu, Wenhan Cao, Chang Liu +3
Estimating hidden states in dynamical systems, also known as optimal filtering, is a long-standing problem in various fields of science and engineering. In this paper, we introduce…
Design and Experimental Test of Datatic Approximate Optimal Filter in Nonlinear Dynamic Systems
Weixian He, Zeyu He, Wenhan Cao +5
Filtering is crucial in engineering fields, providing vital state estimation for control systems. However, the nonlinear nature of complex systems and the presence of non-Gaussian…
NANO-SLAM : Natural Gradient Gaussian Approximation for Vehicle SLAM
Tianyi Zhang, Wenhan Cao, Chang Liu +3
Accurate localization is a challenging task for autonomous vehicles, particularly in GPS-denied environments such as urban canyons and tunnels. In these scenarios, simultaneous loc…
Robust State Estimation for Legged Robots with Dual Beta Kalman Filter
Tianyi Zhang, Wenhan Cao, Chang Liu +3
Existing state estimation algorithms for legged robots that rely on proprioceptive sensors often overlook foot slippage and leg deformation in the physical world, leading to large…
Nonlinear Bayesian Filtering with Natural Gradient Gaussian Approximation
Wenhan Cao, Tianyi Zhang, Zeju Sun +3
Practical Bayes filters often assume the state distribution of each time step to be Gaussian for computational tractability, resulting in the so-called Gaussian filters. When facin…
Convolutional Unscented Kalman Filter for Multi-Object Tracking with Outliers
Shiqi Liu, Wenhan Cao, Chang Liu +2
Multi-object tracking (MOT) is an essential technique for navigation in autonomous driving. In tracking-by-detection systems, biases, false positives, and misses, which are referre…
Convolutional Bayesian Filtering
Wenhan Cao, Shiqi Liu, Chang Liu +3
Bayesian filtering serves as the mainstream framework of state estimation in dynamic systems. Its standard version utilizes total probability rule and Bayes' law alternatively, whe…