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Depth Perception and Spatial Vision

Depth Perception and Spatial Vision

Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.

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Visual Sensing and Depth Perception for Welding Robots and Their Industrial Applications.

Ji Wang^1,2, Leijun Li¹, Peiquan Xu²

¹Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada.

Sensors (Basel, Switzerland)

|December 23, 2023

Summary

This review examines depth perception for intelligent welding robots, assessing sensing methods and deep learning applications. Future research focuses on advanced AI and sensor fusion for enhanced robotic welding quality.

Keywords:

3D reconstruction deep learning depth perception industrial applications welding robot welding sensor

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Area of Science:

Robotics and Artificial Intelligence
Computer Vision
Manufacturing Technology

Background:

Advancements in vision sensing, AI, and robotics necessitate improved sensors for intelligent welding manufacturing.
Depth perception is a critical bottleneck hindering the development of advanced welding sensors.

Purpose of the Study:

To review and assess active and passive sensing methods for depth perception in robotic welding.
To explore deep learning applications for enhancing depth perception in robotic welding processes.
To analyze the current state and future directions of visual perception for welding robots.

Main Methods:

Classification and elaboration of depth perception mechanisms based on monocular, binocular, and multi-view vision.
Exploration of deep learning principles for robotic welding depth perception.
Analysis of 2662 articles, citing 152 references.

Main Results:

Assessment of various depth perception methods, including active and passive sensing.
Discussion on the application of deep learning in robotic welding visual perception across industrial scenarios.
Identification of current challenges and proposed countermeasures in welding robot visual perception technology.

Conclusions:

Future research should focus on deep learning for object detection, transfer learning for robot adaptation, and multi-modal sensor fusion.
Integration of models and hardware, alongside expert collaboration, is crucial for designing effective multi-modal sensor fusion architectures.
Addressing current limitations and exploring novel approaches will drive the evolution of intelligent welding manufacturing.