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Viewpoint Planning for Range Sensors Using Feature Cluster Constrained Spaces for Robot Vision Systems.

Alejandro Magaña¹, Michiel Vlaeyen^2,3, Han Haitjema^2,3

¹Institute for Machine Tools and Industrial Management, Technical University of Munich, 85747 Garching, Germany.

Sensors (Basel, Switzerland)

|September 28, 2023

Summary

This study introduces generalized feature cluster constrained spaces (GC-spaces) for robot vision systems. GC-spaces efficiently find multiple viewpoints for complex multi-feature tasks, improving robot vision performance.

Keywords:

constraint planning range sensor robot vision system viewpoint planning problem vision system automation

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Area of Science:

Robotics
Computer Vision
Computational Geometry

Background:

Robot vision systems (RVS) face challenges computing viewpoints for multi-feature tasks.
Real-world applications require robust viewpoints considering system constraints and uncertainties.
Previous work addressed single-feature viewpoint computation using constrained spaces (C-spaces).

Purpose of the Study:

Extend C-spaces to handle multi-feature viewpoint computation.
Introduce generalized feature cluster constrained spaces (GC-spaces) for multi-feature problems.
Develop a generic viewpoint planning strategy using GC-spaces for efficient multi-feature vision tasks.

Main Methods:

Formulated viewpoint computation as a geometrical problem.
Developed feature cluster constrained spaces (GC-spaces) as a closed-form geometrical solution.
Proposed a generic viewpoint planning strategy based on GC-spaces.

Main Results:

GC-spaces provide an infinite set of valid viewpoints for feature clusters.
The proposed strategy effectively and efficiently solves multi-feature vision tasks.
Validated the framework on industrial dimensional metrology systems.

Conclusions:

GC-spaces offer a robust geometrical solution for multi-feature viewpoint planning in RVS.
The developed strategy enhances the efficiency and effectiveness of robot vision systems in complex scenarios.
The framework is applicable to real-world industrial vision tasks like dimensional metrology.