Machine Vision in the Context of Robotics: A Systematic Literature Review

TL;DR

This systematic review analyzes the last decade of machine vision research in robotics, highlighting progress in robustness and computation efficiency, while identifying ongoing challenges like occlusion and lighting variability.

Contribution

It provides a comprehensive, reproducible overview of recent advances and persistent challenges in machine vision for robotics based on a systematic literature review.

Findings

Robustness and computation time have significantly improved.

Occlusion and lighting variance remain major challenges.

The field shows sustained research interest and ongoing challenges.

Abstract

Machine vision is critical to robotics due to a wide range of applications which rely on input from visual sensors such as autonomous mobile robots and smart production systems. To create the smart homes and systems of tomorrow, an overview about current challenges in the research field would be of use to identify further possible directions, created in a systematic and reproducible manner. In this work a systematic literature review was conducted covering research from the last 10 years. We screened 172 papers from four databases and selected 52 relevant papers. While robustness and computation time were improved greatly, occlusion and lighting variance are still the biggest problems faced. From the number of recent publications, we conclude that the observed field is of relevance and interest to the research community. Further challenges arise in many areas of the field.