Data Fusion and 3D Geometric Modeling from Multi-scale Sensors

Dmitry Tansky, Anath Fischer

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

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.

Original languageEnglish
Title of host publicationLecture Notes in Production Engineering
PublisherSpringer Nature
Pages345-355
Number of pages11
DOIs
StatePublished - 2013

Publication series

NameLecture Notes in Production Engineering
VolumePart F1158

Keywords

  • data fusion
  • inspection analysis
  • multi-scale
  • multi-sensors

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality
  • Economics, Econometrics and Finance (miscellaneous)
  • Industrial and Manufacturing Engineering

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