Dual-Branch INS/GNSS Fusion with Inequality and Equality Constraints
🤖 AI Summary
This study addresses the degradation of vehicle navigation reliability in urban environments due to GNSS signal blockage and the rapid error accumulation of low-cost inertial sensors during GNSS outages. To enhance navigation robustness without additional hardware, the authors propose a dual-branch information-aided framework that integrates equality constraints via variance-weighted fusion with physics-driven inequality motion constraints. Implemented purely as a software enhancement to existing filters, the method effectively improves continuity and suppresses drift. Experimental results demonstrate that under nominal GNSS conditions, vertical positioning error is reduced by 16.7% and altitude accuracy improved by 50.1%; during GNSS-denied scenarios, vertical drift is mitigated by 24.2% and altitude accuracy enhanced by 20.2%.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
urban navigation
GNSS outages
inertial sensor drift
motion constraints
non-holonomic constraint
Innovation
Methods, ideas, or system contributions that make the work stand out.
dual-branch fusion
inequality constraints
equality constraints
INS/GNSS integration
urban navigation
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