Abstract
The past several decades have seen major advances in sensor technologies, including surface scanning at multi-scales. While state-of-the-art research focuses on methods for integrating diverse scanned data into a single geometric model for inspection analysis, these methods still cannot handle multi-scale data. This paper proposes a new approach for data fusion from multi-scale sensors by defining two generic frameworks for data fusion: Single-Level Multi-Sensor (SLMS) for multi-scale data merged on one level and Hierarchical Multi-Sensor (HMS) for hierarchically merged multi-scale data. These frameworks are based on state-of-the-art generic frameworks and use the properties of multi-scale sensors properties. The feasibility of the proposed approach is demonstrated on 2.5D surfaces scanned by CMM touch probes and laser scanners and on 3D multi-scale synthetic data from CAD models.
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Tansky, D., Fischer, A. (2013). Data Fusion and 3D Geometric Modeling from Multi-scale Sensors. In: Abramovici, M., Stark, R. (eds) Smart Product Engineering. Lecture Notes in Production Engineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30817-8_34
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DOI: https://doi.org/10.1007/978-3-642-30817-8_34
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