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Scene Acquisition with Multiple 2D and 3D Optical Sensors: A PSO-Based Visibility Optimization.

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  • 1Department of Industrial Engineering of Florence, University of Florence, Via di S. Marta 3, 50139 Firenze, Italy.

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Summary

This study introduces an automated Particle Swarm Optimization (PSO) method to efficiently configure multiple optical sensors for 3D data acquisition. The system optimizes sensor placement, enhancing data capture and reducing design time for applications like body scanning.

Keywords:
3D scanningPSObody scannercomputer graphicsoptical sensorssensor placementvisibility analysis

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Area of Science:

  • Computer Vision
  • Robotics
  • Sensor Systems Engineering

Background:

  • Designing 3D data acquisition systems requires intensive sensor placement optimization.
  • Sensor positioning is critical for maximizing coverage and system efficacy.
  • Current methods are often manual and time-consuming.

Purpose of the Study:

  • To propose an automated optimization procedure for sensor placement in 3D data acquisition systems.
  • To develop a fast and efficient tool for configuring multiple optical sensors.
  • To introduce generalizable objective functions and filtering techniques for enhanced performance.

Main Methods:

  • Utilized the Particle Swarm Optimization (PSO) algorithm for sensor placement.
  • Developed and described three general objective functions for optimization.
  • Implemented filters to reduce computational time.
  • Addressed the challenge of occlusions from scene obstacles.

Main Results:

  • Demonstrated an automated approach to optimize sensor configuration for 3D data acquisition.
  • The proposed method offers a significant reduction in the design phase labor.
  • The system effectively handles occlusions, improving data integrity.
  • Validated the method through case studies including a body scanner and internal environment acquisition.

Conclusions:

  • The Particle Swarm Optimization (PSO) based method provides an efficient and automated solution for sensor placement in 3D acquisition systems.
  • The approach is versatile, applicable to various scenarios including body scanning and environmental mapping.
  • Future research can leverage the proposed objective functions and filtering techniques for further advancements in sensor system design.