GNSS / INS coupling positioning method and system based on factor graph optimization

The GNSS/INS coupled positioning method optimized by factor graphs utilizes joint optimization within a sliding window and a robust kernel function to design a time-varying covariance model. This solves the problems of linearization error accumulation and filter divergence in complex environments that traditional methods face, achieving high-precision and robust positioning results.

CN121806080BActive Publication Date: 2026-06-19NAT INNOVATION INST OF DEFENSE TECH PLA ACAD OF MILITARY SCI
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
NAT INNOVATION INST OF DEFENSE TECH PLA ACAD OF MILITARY SCI
Filing Date
2026-03-12
Publication Date
2026-06-19

AI Technical Summary

Technical Problem

Existing GNSS/INS compact combination methods based on extended Kalman filtering are prone to linearization error accumulation and filter divergence in complex environments, resulting in unreliable positioning results. In particular, they suffer from slow convergence speed, low positioning accuracy, and poor robustness in environments such as urban canyons.

Method used

A GNSS/INS coupled positioning method based on factor graph optimization is adopted. Nonlinear errors are eliminated by joint optimization within a sliding window and multiple iterations of linearization. A robust kernel function is introduced to suppress abnormal observations. A time-varying covariance model based on signal quality is designed, and nonlinear least squares optimization is performed by combining IMU pre-integration factor and robust GNSS observation factor.

Benefits of technology

It significantly improves the convergence speed of positioning and robustness in complex environments, enhances positioning accuracy and trajectory smoothness, and can maintain high-precision positioning in scenarios with discontinuous signals, such as urban canyons.

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Abstract

This invention belongs to the field of satellite navigation and inertial navigation combined positioning technology, and provides a GNSS / INS coupled positioning method and system based on factor graph optimization. It constructs a time-varying covariance model based on signal quality using time-synchronized and aligned observation data and augmented data, and obtains the time-varying covariance matrix corresponding to each epoch. In the factor graph, robust GNSS observation factors are constructed by comparing the residuals obtained from observed values ​​and state estimation theoretical values ​​with a set residual threshold through GNSS observation factors. A factor graph probabilistic model is constructed by combining prior information. Based on the constructed factor graph probabilistic model, the IMU pre-integration factor, the time-varying covariance matrix, and the robust GNSS observation factors are fused, transforming probabilistic inference into a nonlinear least squares optimization problem, and solving it yields the optimal state estimate. This significantly improves the convergence speed and robustness of positioning in complex environments.
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Citation Information

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