This tutorial presents the factor graph, a recently introduced estimation framework that is a
generalization of the Kalman filter. An approach for constructing a factor graph, with its …

The increasing penetration of distributed renewable generation has introduced significant
uncertainties and randomness to the power distribution network operation. Accurate and …

Robust state estimation is critical for enabling reliable autonomous robot operations in
challenging environments. To estimate the state, heterogeneous sensor fusion is commonly …

Factor graphs have recently emerged as an alternative solution method for GNSS
positioning. In this article, we review how factor graphs are implemented in GNSS, some of …

Recent advances in the fields of robotics and automation have spurred significant interest in
robust state estimation. To enable robust state estimation, several methodologies have been …

The present work focuses on an open problem in the design of Extended Kalman filters: the
lack of knowledge of the measurement noise covariance. A novel extension of the analytic …

In the application of precise point positioning (PPP), especially in the dynamic mode, the
classical Kalman filter (KF) usually produces a large number of estimation errors or diverges …

Snapshot GNSS is a more energy-efficient approach to location estimation than traditional
GNSS positioning methods. This is beneficial for applications with long deployments on …

The online estimation of the process noise covariance matrix (PNCM) in the inertial-based
integrated navigation has always been a challenge due to the small magnitude of the PNCM …

This paper presents a method to estimate the covariances of the inputs in a factor-graph
formulation for localization under non-line-of-sight conditions. A general solution based on …