- 1Department of Geography and Regional Science, University of Graz, Graz, Austria (thomas.goelles@uni-graz.at)
- 2Electrics/Electronics & Software, Virtual Vehicle Research GmbH, Graz, Austria
- 3Industrial Management, FH Joanneum, Kapfenberg, Austria
- 4Geology, University of Vienna, Vienna, Austria
The rapid advancement and commercialization of low-cost (0.5k to 20k EUR) lidar have transformed how researchers monitor and analyze dynamic environmental processes. Initially developed for industries like automotive navigation and robotics, these compact and cost-effective sensors have gained significant traction in geoscience. Many of these sensors operate in the wavelength range of 900 to 1000 nm, where ice is highly reflective, making them particularly suited for cryospheric observations. In addition to their affordability, these systems are robust, capable of high scan rates of up to 20 Hz, and have a range of up to 450 meters. The high scan rates enable the collection of detailed datasets but can result in substantial data volumes that require efficient processing. Many sensors also include integrated IMUs, adding another layer of functionality.
This work focuses on the static use of lidar systems, where they are mounted on fixed structures for continuous or periodic monitoring. Low-cost lidars are typically sold without essential components such as power supplies, data loggers, or data transmission capabilities. Additionally, they often output data in proprietary formats, making data analytics and processing cumbersome. Furthermore, the comparable low range makes it often necessary to use multiple sensors which increases complexity even more. These barriers make their deployment challenging for many researchers or research groups that lack the resources or expertise to build custom solutions.
To address this gap, we have developed a comprehensive system combining hardware, software, and analytical tools to lower the barrier to entry. Our data logger is built on the Robot Operating System (ROS 2), enabling seamless integration of multiple sensors, even from different manufacturers, if they provide a ROS 2 driver. Users can configure scanning intervals and durations to suit their needs, such as a 5-second scan every 15 minutes combined with continuous monitoring. The collected scan data is uploaded to our server via a REST API, where further processing is automated. Our REST API handles tasks such as quality checks, conversion to standard point cloud formats like LAS or CSV, point cloud differencing, and volume calculations. Furthermore, our system integrates seamlessly with pointcloudset, our open-source Python package designed for advanced 4D point cloud analytics. This package enables detailed analysis of extensive point cloud datasets recorded over time.
We present the current version of our API, available at api.avalanchemonitoring.com/schema/swagger, alongside the first deployment of a system with two lidar from Livox in December 2024 in Lech am Arlberg, Austria, at an elevation of 2270 m asl. Preliminary insights from the collected data highlight the potential of our system to enable widespread use of (semi-)permanently installed lidars in cryospheric research and beyond. By providing an accessible and integrated solution, we aim to empower researchers to leverage the full capabilities of low-cost lidar systems without the burden of technical challenges.
How to cite: Goelles, T., Wallner, S., Schlager, B., Prokop, A., Gaisberger, C., Schratter, M., and Muckenhuber, S.: Low-Cost lidar as an Easy-to-Use REST API: Permanently Installed Systems for Cryospheric Research and Beyond, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14949, https://doi.org/10.5194/egusphere-egu25-14949, 2025.