外观
论文arXiv 2609.308141 个月前
SeA-RVINS：面向城市导航的语义感知紧耦合RTK-视觉-惯性系统与保相关鲁棒估计
原文链接：SeA-RVINS: Semantic-Aware Tightly Coupled RTK-Visual-Inertial System with Correlation-Preserving Robust Estimation for Urban Navigation（arXiv 2609.30814 · 发布日期：2026-09-25）
SeA-RVINS: Semantic-Aware Tightly Coupled RTK-Visual-Inertial System with Correlation-Preserving Robust Estimation for Urban Navigation
针对城市绝对位姿估计受异常值和错误关联影响，SeA-RVINS结合语义立体前端与保相关RTK鲁棒因子图，并在TEX-CUP路线验证。
核心要点
- 城市环境中GNSS多路径和错误视觉关联会在紧耦合估计器中持续影响可靠位姿估计。
- 系统采用固定滞后因子图，语义感知学习式双目前端先剔除不可靠轨迹，再将地标加入图中。
- 对双差GNSS量测，方法以多种Dynamic Covariance Scaling形式保持共享枢轴相关结构，并提出混合模糊度延续策略。
- 在公共TEX-CUP约20公里路线中，latent-pivot配置达到100%可用性，最大水平误差1.6米；实现已开源。
验证范围与局限：验证范围为公共TEX-CUP数据集约20公里路线，其中约50%为深城市驾驶。
英文摘要 (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
作者：Wang Hu, Bo Wu
来源
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