Differential Barometric Altimetry for Submeter Vertical Localization and Floor Recognition Indoors

TL;DR

This paper introduces a low-cost, differential barometric system integrated with ROS for precise submeter vertical localization and floor recognition in complex indoor environments, outperforming visual or LiDAR-based methods.

Contribution

The paper presents a novel, ROS-compatible differential barometric framework that achieves sub-meter accuracy and reliable floor recognition for indoor mobile robot localization.

Findings

Achieves 0.29 m RMSE in vertical estimation

100% accuracy in floor recognition

Outperforms visual- and LiDAR-based methods

Abstract

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.