发表机构
UC Riverside（加州大学河滨分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
SeA-RVINS提出语义感知紧耦合RTK-视觉-惯性系统，结合鲁棒估计与混合模糊度策略，在20公里城市路线实现100%可用性和1.6米最大误差。
AI 中文摘要
在城市环境中，可靠的绝对位姿估计会受到异常测量和错误时间关联的破坏，这些问题可能持续存在于紧耦合估计器中。全球导航卫星系统（GNSS）观测提供全球参考测量，但易受多径效应影响。视觉-惯性传感提供局部运动约束，但错误的视觉关联可能破坏估计器。我们提出SeA-RVINS，一种固定滞后因子图实时动态（RTK）视觉-惯性系统，用于鲁棒的城市位姿估计。语义感知的学习型立体前端在持久地标进入图之前拒绝不可靠的轨迹。对于双差GNSS测量，SeA-RVINS通过可配置的批量、标量和潜在枢轴鲁棒公式应用动态协方差缩放，同时保留共享枢轴相关结构。我们提出一种混合模糊度延续策略，在短弧段上共享一个模糊度状态并验证连续性，并通过随机游走因子软连接连续弧段。在公共TEX-CUP数据集约20公里路线上（包括约50%的深度城市驾驶），潜在枢轴配置实现100%可用性和1.6米最大水平误差，分别有96.16%和99.90%的历元低于1.0米和1.5米。该实现已作为开源软件发布。
英文摘要
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
Comments9 pages, 4 figures, 2 tables
