CredibleDFGO: Differentiable Factor Graph Optimization with Credibility Supervision

TL;DR

CredibleDFGO introduces a differentiable GNSS factor graph framework that explicitly optimizes covariance credibility, leading to improved uncertainty estimation and positioning accuracy in urban navigation scenarios.

Contribution

It proposes a novel framework that makes covariance credibility an explicit training target using a neural network and proper scoring rules within a differentiable solver.

Findings

Consistent gains in uncertainty credibility across three UrbanNav scenes.

Improved positioning accuracy in medium-urban and harsh-urban scenes.

Significant reduction in error metrics on the Mong Kok scene, including horizontal error and negative log-likelihood.

Abstract

Global navigation satellite system (GNSS) positioning is widely used for urban navigation, but the covariance reported by the GNSS solver is often unreliable in urban canyons. Existing differentiable factor graph optimization (DFGO) methods already learn measurement weighting through the solver, but they still use position-only objectives. As a result, the mean estimate may improve while the reported covariance remains too small, too large, or wrong in shape. In this work, we propose CredibleDFGO (CDFGO), a differentiable GNSS factor graph framework that makes covariance credibility an explicit training target. The Weighting Generation Network (WGN) predicts per-satellite reliability weights. The differentiable Gauss--Newton solver maps these weights to a position estimate and posterior covariance, and proper scoring rules supervise the East--North predictive distribution end-to-end. We study negative log-likelihood (NLL), Energy Score (ES), and their combination. Results on three UrbanNav test scenes show consistent gains in uncertainty credibility. Positioning accuracy also improves on the medium-urban and harsh-urban scenes, and the mean horizontal error and 95th-percentile error improve on the deep-urban scene. On the harsh-urban Mong Kok (MK) scene, CDFGO-Combined reduces the mean horizontal error from 13.77\,m to 11.68\,m, reduces NLL from 40.63 to 6.59, and reduces ES from 12.31 to 9.05. The case studies link the MK improvement to better axis-wise consistency, more credible local covariance ellipses, and satellite-level reweighting.