Abstract
Multi-sensor data fusion depicts a challenge if the data collected from different remote sensors present with diverging properties such as point density, noise and outliers. To overcome a time-consuming, manual sensor-registration procedure in rail-track data, the TransMVS COMET project was initiated in a joint collaboration between the company Track Machines Connected and the research institute Software Competence Center Hagenberg. One of the project aims was to develop a semi-automated and robust data fusion workflow allowing to combine multi-sensor data and to extract the underlying matrix transformation solving the multi-sensor registration problem. In addition, the buildup and transfer of knowledge with respect to 3D point cloud data analysis and registration was desired. Within a highly interactive approach, a semi-automated workflow fulfilling all requirements could be developed, relying on a close collaboration between the partners. The knowledge gained within the project was transferred in multiple partner meetings, leading to a knowledge leap in 3D point cloud data analysis and registration for both parties.
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Acknowledements
The research reported in this paper has been funded by the Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology (BMK), the Federal Ministry for Digital and Economic Affairs (BMDW), and the State of Upper Austria in the frame of SCCH, a center in the COMET - Competence Centers for Excellent Technologies Programme managed by Austrian Research Promotion Agency FFG.
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Kromp, F., Hinterberger, F., Konanur, D., Wieser, V. (2022). Collaborative Aspects of Solving Rail-Track Multi-sensor Data Fusion. In: Kotsis, G., et al. Database and Expert Systems Applications - DEXA 2022 Workshops. DEXA 2022. Communications in Computer and Information Science, vol 1633. Springer, Cham. https://doi.org/10.1007/978-3-031-14343-4_7
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