论文/arXiv 2605.24767·21 天前

使用基于 GNSS 的加速度测量增强 INS/GNSS 状态估计

针对低动态下标准 INS/GNSS 可观性受限，利用历史 GNSS 与运动模型提取加速度，并在真实无人地面车数据上验证。

AI 整理

• 感知与导航
• 数据与采集
• 端侧部署

原文链接：Enhanced INS/GNSS State Estimation using GNSS-Based Acceleration Measurements（arXiv 2605.24767 · 发布日期：2026-10-07）

Enhanced INS/GNSS State Estimation using GNSS-Based Acceleration Measurements

针对低动态下标准 INS/GNSS 可观性受限，利用历史 GNSS 与运动模型提取加速度，并在真实无人地面车数据上验证。

核心要点

• 自动地面车辆需要准确可靠导航，标准 GNSS 位置更新对姿态和惯性传感器误差状态观测有限。
• 方法利用过去 GNSS 测量结合运动模型提取车辆加速度信息，并将其融入 INS/GNSS 滤波器。
• 在两套真实无人地面车数据集上，相比标准位置辅助滤波，定位精度表现出一致提升。
• 平均位置 RMSE 在两套数据集上分别提升 11.40% 和 20.74%。

验证范围与局限：仅在两套真实无人地面车辆数据集上验证，数据来自不同移动平台和惯性传感器等级。

英文摘要 (Abstract)

Accurate and reliable navigation is essential for autonomous ground vehicle operations. Standard INS/GNSS fusion relies on GNSS position updates, which provide limited observability of orientation and inertial sensor error states, particularly during low-dynamic motion. In this work, we propose utilizing past GNSS measurements alongside a motion model to extract meaningful vehicle acceleration information. This acceleration measurement is then integrated into the INS/GNSS filter to improve its robustness and accuracy. The proposed approach is evaluated on two real-world unmanned ground vehicle datasets collected from different mobile platforms and inertial sensor grades. Results demonstrate consistent positioning accuracy improvements relative to the standard position-aided filter, with mean position root mean square error improvements of 11.40% and 20.74% on the two datasets, respectively.

作者：Gal Versano, Itzik Klein

来源

本页事实对应的公开页面，以原文为准。

1. arXiv 2605.24767：Enhanced INS/GNSS State Estimation using GNSS-Based Acceleration Measurements访问 2026-10-21