Feature and Viewpoint Selection for Industrial Car Assembly

The efficient computation of viewpoints while considering various system and process constraints is a common challenge that any robot vision system is confronted with when trying to execute a vision task. Although fundamental research has provided solid and sound solutions for tackling this problem, a holistic framework that poses its formal description, considers the heterogeneity of robot vision systems, and offers an integrated solution remains unaddressed. Hence, this publication outlines the generation of viewpoints as a geometrical problem and introduces a generalized theoretical framework based on Feature-Based Constrained Spaces (C-spaces) as the backbone for solving it. A C-space can be understood as the topological space that a viewpoint constraint spans, where the sensor can be positioned for acquiring a feature while fulfilling the constraint. The present study demonstrates that many viewpoint constraints can be efficiently formulated as C-spaces, providing geometric, deterministic, and closed solutions. The introduced C-spaces are characterized based on generic domain and viewpoint constraints models to ease the transferability of the present framework to different applications and robot vision systems. The effectiveness and efficiency of the concepts introduced are verified on a simulation-based scenario and validated on a real robot vision system comprising two different sensors.

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“…Similarly, Refs. [27,28] introduced the concept of visibility maps to encode feature visibility mapped to a visibility sphere. Years later, Refs.…”

Section: Model-basedmentioning

confidence: 99%

Viewpoint Generation Using Feature-Based Constrained Spaces for Robot Vision Systems

et al. 2023

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“…The number of necessary viewpoints is equivalent to the length of the calculated path and typically comprises several dozens of viewpoints. Stößel et al 8 introduce a model-based method to calculate a viewpoint from which combined objects like nuts and bolts can be inspected best. In that paper, the collective viewpoint entropy is used to express the amount of information conveyed in a certain scene that is being watched from a given viewpoint.…”

Section: State Of the Art In Viewpoint Selection Strategies For Indusmentioning

confidence: 99%

“…As a way to determine the visibility of a 1D point feature, the term visibility map was first introduced by Stößel et al 8 It is defined for points lying on the surface of the inspected object. The visibility map is calculated by projecting the inspected object (and possibly the whole scene) onto a unit sphere centered at the point on the object for which it is calculated.…”

Section: Visibility Mapmentioning

confidence: 99%

Model-based automatic calculation and evaluation of camera positions for industrial machine vision

Ellenrieder

Komoto

2005

SPIE Proceedings

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One of the key issues for a successful inspection process is the determination of the necessary number of cameras and their respective positions given a specific inspection task and a geometric model of the inspected work-piece and its surroundings. In the last decades, a number of approaches concerning camera positioning strategies have been proposed. Generally, these approaches define an inspection task in terms of good visibility of certain features on the surface of the inspected objects. However, these approaches neither provide general means to include arbitrary inspection requirements, nor do they minimize the number of required cameras. Others use only hard constraints to determine the area of feasibility for certain task requirements. To overcome these shortcomings, we propose a model-based approach to optimize one or more camera positions by optimizing cost-functions derived from the inspection task. The goal is to use a minimum number of cameras / camera positions to fulfill the inspection task. Feature-visibility is represented using a novel concept: the visibility map. It can be calculated quickly by using a projective approach, consumes little storage memory and allows for quick feature-visibility checks. The system is evaluated on several examples using real inspection tasks from current production processes.

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“…Stel et al [10] extend the notion of CLFs to an entropy based viewpoint selection method. There, the best viewpoints for distinguishing different aggregate models, i.e.…”

Section: Entropy Based Viewpoint Selectionmentioning

confidence: 99%

A Versatile Model-Based Visibility Measure for Geometric Primitives

et al. 2005

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Abstract. In this paper, we introduce a novel model-based visibility measure for geometric primitives called visibility map. It is simple to calculate, memory efficient, accurate for viewpoints outside the convex hull of the object and versatile in terms of possible applications. Several useful properties of visibility maps that show their superiority to existing visibility measures are derived. Various example applications from the automotive industry where the presented measure is used successfully conclude the paper.

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Feature and Viewpoint Selection for Industrial Car Assembly

Cited by 7 publications

References 10 publications

Viewpoint Generation Using Feature-Based Constrained Spaces for Robot Vision Systems

Viewpoint Generation Using Feature-Based Constrained Spaces for Robot Vision Systems

Model-based automatic calculation and evaluation of camera positions for industrial machine vision

A Versatile Model-Based Visibility Measure for Geometric Primitives

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