用差分气压传感实现室内亚米级高精度垂直定位与楼层识别
Differential Barometric Altimetry for Submeter Vertical Localization and Floor Recognition Indoors
- 通过差分气压传感融合基站与移动传感器数据，消除漂移
- 在封闭楼梯间和电梯中实现0.29米均方根误差和100%楼层识别率
- 低成本开源方案，适合真实机器人场景的垂直感知需求
在复杂多层环境中，精确的高度估计和可靠的楼层识别对移动机器人定位与导航至关重要。本文提出一种鲁棒、低成本的垂直估计框架，利用集成于完整ROS兼容软件包中的差分气压传感技术。系统同时发布静止基站和移动传感器的实时高度数据，实现无漂移的高精度垂直定位。在封闭楼梯间和电梯等挑战性场景下的实证评估表明，所提气压处理流程在垂直方向达到亚米级精度（均方根误差：0.29米），并实现100%的楼层识别率。相比之下，仅依赖视觉或激光雷达SLAM里程计的高度估计无法满足可靠垂直定位需求。因此，该开源的ROS兼容气压模块为实际机器人部署提供了实用且经济的垂直感知解决方案。代码已公开于https://github.com/witsir/differential-barometric。
原文摘要 · Abstract (English)
Accurate altitude estimation and reliable floor recognition are critical for mobile robot localization and navigation within complex multi-storey environments. In this paper, we present a robust, low-cost vertical estimation framework leveraging differential barometric sensing integrated within a fully ROS-compliant software package. Our system simultaneously publishes real-time altitude data from both a stationary base station and a mobile sensor, enabling precise and drift-free vertical localization. Empirical evaluations conducted in challenging scenarios -- such as fully enclosed stairwells and elevators, demonstrate that our proposed barometric pipeline achieves sub-meter vertical accuracy (RMSE: 0.29 m) and perfect (100%) floor-level identification. In contrast, our results confirm that standalone height estimates, obtained solely from visual- or LiDAR-based SLAM odometry, are insufficient for reliable vertical localization. The proposed ROS-compatible barometric module thus provides a practical and cost-effective solution for robust vertical awareness in real-world robotic deployments. The implementation of our method is released as open source at https://github.com/witsir/differential-barometric.
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