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.

翻译：精确的高度估计与可靠的楼层识别对于移动机器人在复杂多层环境中的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，该框架利用差分气压传感技术，并集成于完全兼容ROS的软件包中。我们的系统可同时发布来自固定基站与移动传感器的实时高度数据，从而实现精确且无漂移的垂直定位。在极具挑战性的场景（如全封闭楼梯间和电梯）中进行的实证评估表明，所提出的气压测量流程实现了亚米级垂直精度（均方根误差：0.29米）与完美的楼层识别准确率（100%）。相比之下，我们的结果证实，仅基于视觉或激光雷达的SLAM里程计获得的独立高度估计不足以实现可靠的垂直定位。因此，所提出的ROS兼容气压模块为实际机器人部署中的鲁棒垂直感知提供了一种实用且经济高效的解决方案。本方法的实现已在https://github.com/witsir/differential-barometric开源发布。