SeA-RVINS: Semantic-Aware Tightly Coupled RTK-Visual-Inertial System with Correlation-Preserving Robust Estimation for Urban Navigation

Wang Hu; Bo Wu

cs.ROSep 25, 2026

SeA-RVINS: Semantic-Aware Tightly Coupled RTK-Visual-Inertial System with Correlation-Preserving Robust Estimation for Urban Navigation

Authors: Wang Hu, Bo Wu

Organizations: UC Riverside

Abstract

Reliable absolute pose estimation in urban environments is undermined by outlier measurements and incorrect temporal associations that can persist in tightly coupled estimators. Global Navigation Satellite System (GNSS) observations provide globally referenced measurements but are prone to multipath effects. Visual-inertial sensing supplies local motion constraints, but false visual associations can corrupt the estimator. We present SeA-RVINS, a fixed-lag factor-graph Real-Time Kinematic (RTK) visual-inertial system for robust urban pose estimation. A semantic-aware learned stereo frontend rejects unreliable tracks before persistent landmarks enter the graph. For double-differenced GNSS measurements, SeA-RVINS applies Dynamic Covariance Scaling through configurable batch, scalar, and latent-pivot robust formulations while retaining the shared-pivot correlation structure. We propose a hybrid ambiguity-continuation strategy that shares one ambiguity state over short arcs with verified continuity and softly links successive arcs through random-walk factors. On an approximately 20-km route from the public TEX-CUP dataset, including about 50% deep-urban driving, the latent-pivot configuration achieves 100% availability and a 1.6-m maximum horizontal error, with 96.16% and 99.90% of epochs below 1.0 and 1.5 m, respectively. The implementation is released as open-source software

Figures & tables

Fig. 1: System overview and schematic Factor Graph Optimization. Latent pivot variables and marginalization are omitted for clarity. The stereo frontend is detailed in Fig. 2 .

Fig. 2: Workflow of the semantic-aware learned stereo frontend for feature tracking and landmark admission.

Method	Configuration			Horizontal error					3-D error			Avail. (%)
Method	GNSS	Vision	IMU	RMSE (m)	Max. (m)	$<1.0$ m (%)	$<1.5$ m (%)	P95 (m)	RMSE (m)	Max. (m)	P95 (m)	Avail. (%)
RTKLIB-EX	r+b	$\times$	$\times$	4.95	58.56	46.14%	59.23%	8.41	10.31	66.99	21.52	100.0%
GICI-RTK [ 16 ]	r+b	$\times$	$\times$	4.69	177.00	77.60%	87.43%	5.70	7.25	265.26	8.64	96.7%
GICI-RRR [ 16 ]	TC, r+b	M	$\checkmark$	92.44	1122.59	48.54%	55.97%	140.57	92.99	1122.73	147.72	96.3%
VINS-Fusion [ 38 ]	LC	S	$\checkmark$	5.10	35.86	44.26%	57.82%	9.68	10.48	59.07	22.38	99.9%
IC-GVINS [ 39 ]	LC	M	$\checkmark$	7.68	30.47	9.16%	14.31%	21.95	8.10	30.47	21.97	37.2%

TABLE I: Sensor configurations and positioning performance on the evaluated TEX-CUP route.

Method	Used-fix epochs	Fix rate (%)	Horizontal error
Method	Used-fix epochs	Fix rate (%)	$<0.5$ m (%)	$\geq 0.5$ m (epochs)	RMSE (m)	Max. (m)
SeA-RVINS (batch)	1383	34.23	97.54	34	0.17	0.98
SeA-RVINS (scalar)	1915	47.40	93.99	115	0.20	0.85
SeA-RVINS (latent)	1842	45.59	99.35	12	0.15	0.55

TABLE II: Integer Ambiguity Resolution-fixed solution summary. Horizontal-error statistics are conditional on used-fix epochs.

Fig. 3: Cumulative horizontal-error distributions for selected methods. Vertical dashed lines mark 1.0 and 1.5 m.

Fig. 4: Estimated trajectories and ground truth in four selected downtown Austin segments. Imagery: © 2026 Google (imagery date: Dec. 3, 2023).

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Visual-Inertial Odometry Multimodal Sensor Fusion

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Robust and accurate pose estimation with multi-modal sensors is fundamental for autonomous vehicles and mobile robotic systems in complex environments. In this paper, we propose DAP-Pose, a unified end-to-end model for robust multi-modal pose estimation. DAP-Pose introduces a Bi-level Cross-modal Fusion (BCF) module that captures complementary semantic and geometric motion cues from visual, inertial, and GNSS measurements. To handle temporal offsets, we designed a Deep Temporal Alignment (DTA) module that explicitly aligns asynchronous streams in latent space, enabling coherent motion modeling without strict hardware synchronization. Furthermore, we incorporate physics-aware constraints via manifold geometry and GNSS-guided absolute metric scale, enforcing motion consistency and mitigating drift. Experiments upon the public KITTI benchmark dataset were conducted to evaluate the performance of DAP-Pose against existing methods. DAP-Pose achieved the state-of-the-art performance, with the lowest average translation error (

t_{rel}

) of 1.31% and rotation error (

r_{rel}

) of 0.46

^{\circ}

. Furthermore, it accurately estimates poses and maintains robust performance under severe artificially injected temporal misalignment.

Abstract

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