Back to the Future: Revisiting Barometric Levelling

Back to the Future: Revisiting Barometric Levelling

Barometric levelling, traditionally employed in aviation, parachute jumping, and mountain climbing, has undergone a transformative shift with the technological advancement of state-of-the-art atmospheric pressure sensors. This evolution has rendered these sensors more accurate and affordable, broadening their applications within the realm of Intelligent Systems. The integration of barometric altimeter modules into electronic devices, coupled with GNSS and mapping systems, has significantly enhanced their versatility, making them complementary components in various consumer electronics like smartphones, watches, sports bands, bicycle computers, and motor vehicle tracking devices. Moreover, they play a pivotal role in emerging technologies such as drone mapping projects and modelling. This because of their capabilities of measuring elevations in GNSS denied areas such as caves, canyons, and tunnels. The significance of barometric levelling lies in its capacity to determine relative elevation between two points through the measurement of atmospheric pressure. This technique leverages the fact that atmospheric pressure decreases with increasing altitude, enabling the calculation of elevation differences by comparing pressure readings at different locations.
In this study, 25 elevations within a 3 km radius were independently measured using barometric levelling, highlighting its practical usage. These elevations ranged from 2 m to 140 m. Readings of time of measurements, temperature, air pressure, and humidity were taken at each of these stations and were synchronized with the readings obtained from a fixed reference station of known elevation. The measurements at this reference station were carried out with an instrument with data logging capabilities, able to record relevant weather data at pre-established intervals of time without human intervention. The results of these measurements were compared to fixed reference stations of known elevation, and the subsequent differences were analysed. The study employed a portable weather monitoring instrumentation with data logging capabilities, emphasizing the importance of automated data collection practices. The comparison of the 25 elevation readings with true elevations, determined by conventional surveying methods, revealed an RMSE of +/- 0.49 meters, affirming the accuracy of barometric levelling.
Additionally, a distance-dependent test demonstrated a predictable decrease in positional accuracy as the distance from a reference base station increased, providing valuable insights into the system's limitations. The barometric levelling tests presented here offer results that can inform future applications. The conclusions and recommendations derived from this study provide guidance for optimizing the use of barometric levelling within the broader landscape of Intelligent Systems.