Faster accurate position error bounds for gnss imu in urban environments
Incremental-Smoothing-Based ARAIM Protection Levels for Tightly Coupled GNSS/IMU Integration in Urban Canyons
Robotics
Summary
Positioning systems in cities can be less reliable due to signal issues. The authors developed a new way to calculate safety margins (protection levels) for combined GPS and motion sensor data that keeps these margins accurate and reliable even with complex sensor interactions. They also made the calculations much faster, keeping tracking accurate and safety checks effective during tests on real roads.
What this means in practice
- •For automotive navigation engineers: Implement faster and reliable integrity checks for vehicle positioning systems in urban canyon conditions using tightly coupled GNSS/IMU data.
- •For drone operations teams: Enhance urban drone navigation safety by applying improved protection levels computed incrementally with GNSS/IMU fusion.
Authors
Jiahao Hu, Xiangru Wang, Weisong Wen, Meng Qian
Abstract
Protection levels (PLs) bound position errors under specified fault hypotheses and risk budgets, but advanced re-ceiver autonomous integrity monitoring (ARAIM) is formulated for snapshot GNSS estimation rather than the correlated poste-rior of tightly coupled GNSS/IMU factor graph optimization (FGO). This paper formulates ARAIM solution separation and PL computation on the FGO posterior. Because current-epoch pseudorange factors enter only the current-state block of the fixed-lag information matrix, the fault-excluded position covari-ance, solution separation, and nominal-bias propagation follow exactly from the linearized current-state marginal, which re-tains correlations with velocity, IMU-bias, and clock states and with historical information. Woodbury updates with selected-output evaluation compute these quantities for fault-free, single-satellite, constellation-wide, and second-order hypotheses with-out repeated full-state inversion or graph re-optimization. Sur-rounding monitoring layers screen measurements, handle post-fit outliers, and determine PL validity and availability. In two road experiments, both nominal sequences achieve 100% hori-zontal and vertical empirical bounding over PL-valid epochs, with a maximum error-to-PL ratio of 0.96. Normalized errors show that the posterior covariance alone does not capture slowly varying ranging biases: a covariance-only bound computed from the same posteriors retains no resolvable margin, and strong temporal correlation limits the empirical evidence to consisten-cy checks rather than validation of the integrity budget. On identical Experiment A posteriors, the proposed method reduces fault-hypothesis evaluation and PL solving time by 65.2% rela-tive to direct full-state inversion with matching integrity deci-sions, and controlled fault injections demonstrate fail-safe PL invalidation while navigation continues.