Research Project
HELIOS - Heidelberg LiDAR Operations Simulator
News
HELIOS++ will be available soon - first users may contact us to get hands on the v0.9 beta release. Stay tuned for updates!
Stay up-to-date by following HELIOS in our GIScience News Blog and on Twitter: #HELIOS.
A Blender add-on is available to convert Blender scenes to HELIOS scenes with semantic labels: Blender2Helios (developed by M. Neumann, 2020).
Download and Use HELIOS
HELIOS is open source (GNU GPL) and hosted in our github repository. A pre-compiled version can be downloaded here. The corresponding wiki contains information on how to set up and handle HELIOS.
Some impressions of HELIOS
Publications
- Backes, D., Smigaj, M., Schimka, M., Zahs, V., Grznárová, A., and Scaioni, M (2020): River morphology monitoring of a small-scale alpine riverbed using drone photogrammetry and lidarIn: Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, pp. 1017-1024.
- Schäfer, J., Faßnacht, F., Höfle, B. & Weiser, H.(2019): Das SYSSIFOSS-Projekt: Synthetische 3D-Fernerkundungsdaten für verbesserte Waldinventurmodelle.In: 2. Symposium zur angewandten Satellitenerdbeoachtung, Cologne, Germany, pp.1-1.
- Hämmerle, M., Lukač, N., Chen, K.-C., Koma, Zs., Wang, C.-K., Anders, K., & Höfle, B. (2017): Simulating Various Terrestrial and UAV LiDAR Scanning Configurations for Understory Forest Structure Modelling. In: ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W4, pp. 59-65. DOI: 10.5194/isprs-annals-IV-2-W4-59-2017.
- Bechtold, S., Hämmerle, M. & Höfle, B. (2016): Simulated full-waveform laser scanning of outcrops for development of point cloud analysis algorithms and survey planning: An application for the HELIOS lidar simulation framework. In: Proceedings of the 2nd Virtual Geoscience Conference, Bergen, Norway, 21-23 September 2016, pp. 57-58.
- Bechtold, S. & Höfle, B. (2016): HELIOS: A Multi-purpose LiDAR Simulation Framework for Research, Planning and Training of Laser Scanning Operations With Airborne, Ground-based Mobile and Stationary Platforms. In: ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-3, pp. 161-168. DOI: 10.5194/isprs-annals-III-3-161-2016.
Publications from Other Research Groups
- Li, L., Mu, X., Soma, M., Wan,P., Qi, J., Hu, R., Zhang, W., Tong, Y., Yan, G. (2020): An Iterative-Mode Scan Design of Terrestrial Laser Scanning in Forests for Minimizing Occlusion Effects. In: IEEE Transactions on Geoscience and Remote Sensing. DOI: 10.1109/TGRS.2020.3018643.
- Park, M., Baek, Y., Dinare, M., Lee, D., Park, K.-H., Ahn, J., Kim, D., Medina, J., Choi, W.-J., Kim, S., Zhou, C., Heo, J. & Lee, K. (2020): Hetero-integration enables fast switching time-of-flight sensors for light detection and ranging. In: Sci Rep 10, 2764 (2020), pp. 1-8. DOI: https://doi.org/10.1038/s41598-020-59677-x.
- Schlager, B., Muckenhuber, S., Schmidt, S., Holzer, H. et al. (2020): State-of-the-Art Sensor Models for Virtual Testing of Advanced Driver Assistance Systems/Autonomous Driving Functions. In: SAE International Journal of Connected and Automated Vehicles 3 (3), pp. 233-261. DOI: 10.4271/12-03-03-0018.
- Wang, D. (2020): Unsupervised semantic and instance segmentation of forest point clouds. In: ISPRS Journal of Photogrammetry and Remote Sensing 165 (2020), pp. 86-97. DOI: 10.1016/j.isprsjprs.2020.04.020.
- Wang, D., Schraik, D., Hovi, A., Rautiainen, M.(2020): Direct estimation of photon recollision probability using terrestrial laser scanning. In: Remote Sensing of Environment 247 (2020), pp. 1-12. DOI: 10.1016/j.rse.2020.111932.
- Zhu, X., Liu, J., Skidmore, A.K., Premier, J., & Heurich, M. (2020): A voxel matching method for effective leaf area index estimation in temperate deciduous forests from leaf-on and leaf-off airborne LiDAR data. In: Remote Sensing of Environment, 240. DOI: 10.1016/j.rse.2020.111696.
- Lin, C.-H. & Wang, C.-K. (2019): Point Density Simulation for ALS Survey. In: Proceedings of the 11th International Conference on Mobile Mapping Technology (MMT2019), Shenzhen, China. pp. 157-160.
- Liu, J., Skidmore, A.K., Wang, T., Zhu, X., Premier, J., Heurich, M., Beudert, B. & Jones, S. (2019): Variation of leaf angle distribution quantified by terrestrial LiDAR in natural European beech forest. In: ISPRS Journal of Photogrammetry and Remote Sensing, 148, pp. 208-220. DOI: 10.1016/j.isprsjprs.2019.01.005.
- Liu, J., Wang, T., Skidmore, A.K., Jones, S., Heurich, M., Beudert, B. & Premier, J. (2019): Comparison of terrestrial LiDAR and digital hemispherical photography for estimating leaf angle distribution in European broadleaf beech forests. In: ISPRS Journal of Photogrammetry and Remote Sensing, 158, pp. 76-89. DOI: 10.1016/j.isprsjprs.2019.09.015.
- Martínez Sánchez, J., Álvarez, Á., Vilariño, D., Rivera, F., Cabaleiro, J. & Pena, T. (2019): Fast Ground Filtering of Airborne LiDAR Data Based on Iterative Scan-Line Spline Interpolation. In: Remote Sensing 11, pp. 1-23. DOI: 10.3390/rs11192256.
- Previtali, M., Díaz-Vilariño, L., Scaioni, M. & Frías Nores, E. (2019): Evaluation of the Expected Data Quality in Laser Scanning Surveying of Archaeological Sites. In: 4th International Conference on Metrology for Archaeology and Cultural Heritage, Florence, Italy, 4-6 December 2019, pp. 19-24.
- Xiao, W., Zaforemska, A., Smigaj, M., Wang, Y., Gaulton, R. (2019): Mean Shift Segmentation Assessment for Individual Forest Tree Delineation from Airborne Lidar Data. In: Remote Sensing, 11, pp. 1-19. DOI: https://doi.org/10.3390/rs11111263.
- Zhang, Z., Li, J., Guo, Y. & Yang, C. (2019): 3D Highway Curve Reconstruction From Mobile Laser Scanning Point Clouds. In: IEEE Transactions on Intelligent Transportation Systems, pp. 1-11. DOI: 10.1109/TITS.2019.2946259.
- Rebolj, D., Pučko, Z., Babič, N.Č., Bizjak, M. & Mongus, D. (2017). Point cloud quality requirements for Scan-vs-BIM based automated construction progress monitoring. In: Automation in Construction, 84, pp. 323-334. DOI: 10.1016/j.autcon.2017.09.021.
Objective
In many technical domains of modern society, there is a growing demand for fast, precise and automatic acquisition of digital 3D models of a wide variety of physical objects and environments. Laser scanning is a popular and widely used technology to cover this demand, but it is also expensive and complex to use to its full potential.
There are scenarios in which the operation of a real laser scanner can be replaced by a computer simulation, in order to save time and costs. This includes scenarios like teaching and training of laser scanning, development of new scanner hardware and scanning methods, or generation of artificial scan data sets to support the development of point cloud processing and analysis algorithms.
Following this idea, we develop a highly flexible laser scanning simulation framework named Heidelberg LiDAR Operations Simulator (HELIOS).
Project Tasks
HELIOS is implemented as a Java library and split up into a core component and multiple extension modules. Extensible Markup Language (XML) is used to define scanner, platform and scene models and to configure the behaviour of modules. Modules are developed and implemented for (1) loading of simulation assets and configuration (i.e. 3D scene models, scanner definitions, survey descriptions etc.), (2) playback of XML survey descriptions, (3) TLS survey planning (i.e. automatic computation of recommended scanning positions) and (4) interactive real-time 3D visualization of simulated surveys.
Projects Using Helios
How to Cite HELIOS
If you use HELIOS in your work, please cite:
Bechtold, S. & Höfle, B. (2016): HELIOS: A Multi-Purpose LiDAR Simulation Framework for Research, Planning and Training of Laser Scanning Operations with Airborne, Ground-Based Mobile and Stationary Platforms. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. III-3, pp. 161-168. DOI: 10.5194/isprs-annals-III-3-161-2016
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