Dual-Branch INS/GNSS Fusion with Inequality and Equality Constraints

TL;DR

This paper introduces a dual-branch fusion framework that combines inequality and equality motion constraints to enhance vehicle navigation accuracy and robustness in urban environments, especially during GNSS outages.

Contribution

It proposes a variance-weighted scheme to fuse inequality and equality constraints in a navigation filter without extra sensors or hardware, improving robustness and accuracy.

Findings

Reduces vertical position error by 16.7% under full GNSS

Improves altitude accuracy by 50.1% with the proposed method

Decreases vertical drift by 24.2% during GNSS-denied conditions

Abstract

Reliable vehicle navigation in urban environments remains a challenging problem due to frequent satellite signal blockages caused by tall buildings and complex infrastructure. While fusing inertial reading with satellite positioning in an extended Kalman filter provides short-term navigation continuity, low-cost inertial sensors suffer from rapid error accumulation during prolonged outages. Existing information aiding approaches, such as the non-holonomic constraint, impose rigid equality assumptions on vehicle motion that may be violated under dynamic urban driving conditions, limiting their robustness precisely when aiding is most needed. In this paper, we propose a dual-branch information aiding framework that fuses equality and inequality motion constraints through a variance-weighted scheme, requiring only a software modification to an existing navigation filter with no additional sensors or hardware. The proposed method is evaluated on four publicly available urban datasets featuring various inertial sensors, road conditions, and dynamics, covering a total duration of 4.3 hours of recorded data. Under Full GNSS availability, the method reduces vertical position error by 16.7% and improves altitude accuracy by 50.1% over the standard non-holonomic constraint. Under GNSS-denied conditions, vertical drift is reduced by 24.2% and altitude accuracy improves by 20.2%. These results demonstrate that replacing hard motion equality assumptions with physically motivated inequality bounds is a practical and cost-free strategy for improving navigation resilience, continuity, and drift robustness without relying on additional sensors, map data, or learned models.