AR Overlay for ATM Servicing via 3D Component Recognition
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Solution Overview
Problem
Technicians face challenges in accurately servicing equipment, such as ATMs, due to varying configurations and types, leading to missed tasks, poor quality servicing, and increased training requirements, which results in higher costs and downtime.
Innovation Solution
A user device equipped with computer vision techniques, like SIFT and monoSLAM, generates a 3D model of equipment components to identify the device and provide an augmented reality overlay with specific tasks, enabling accurate and efficient servicing by reducing the need for extensive technician training and improving task completion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If technicians service equipment with varying configurations and types, then the system must handle diverse device variations, but this leads to missed tasks, poor quality servicing, and increased training requirements
Solution Approach 1:
The system performs preliminary actions by capturing images of the equipment before servicing begins, generating a 3D model, and pre-identifying the equipment type and required tasks. This preliminary configuration identification and task list generation ensures that all necessary servicing steps are determined in advance, preventing missed tasks and ensuring consistent quality across different equipment variations.
Solution Approach 2:
The system uses feedback mechanisms by comparing captured equipment images against known equipment profiles to automatically identify equipment type and configuration. This feedback loop enables the system to adapt to varying equipment configurations accurately, providing real-time guidance on the correct servicing tasks for each specific equipment type, thereby maintaining high servicing quality without requiring extensive technician training.
2Reliability
If technicians are provided with extensive training to handle various equipment types, then servicing quality improves, but training costs and time requirements increase
Solution Approach 1:
The system enables self-service by automatically identifying equipment type and configuration through image capture and 3D model generation, then autonomously generating the appropriate task list. This eliminates the need for technicians to manually memorize equipment variations and their associated servicing procedures, providing expert-level guidance through the AR interface without requiring extensive formal training.
Solution Approach 2:
The system replaces the mechanical training process with an automated digital system that uses computer vision, 3D modeling, and AR technology to provide real-time servicing guidance. Instead of investing time in training technicians to recognize and handle various equipment types, the system substitutes this with automated equipment identification and dynamic task list generation, delivering the same quality improvement without the time cost of extensive training.
3Productivity
If manual identification and task determination is used, then equipment and tasks can be serviced, but accuracy decreases and errors increase
Solution Approach 1:
The system transitions from two-dimensional image analysis to three-dimensional model generation, capturing the equipment's spatial structure and configuration more comprehensively. This 3D modeling approach provides more accurate equipment identification and task determination compared to manual methods, reducing errors while maintaining efficient servicing speed through automated processing.
Solution Approach 2:
The system introduces an intermediary layer between the technician and the equipment by using automated image capture, 3D model generation, and AR display. This intermediary system handles the complex tasks of equipment identification and task determination, providing accurate guidance to the technician without requiring manual interpretation, thereby improving both accuracy and efficiency simultaneously.
Data Source
AI summary
A device may detect, in a field of view of a camera, one or more components of an automated teller machine (ATM) device using a computer vision technique based on generating a three dimensional model of the one or more components. The device may identify the ATM device as a particular device or as a particular type of device based on the one or more components of the ATM device, or first information related to the ATM device. The device may identify a set of tasks to be performed with respect to the ATM device. The device may provide, for display via a display associated with the device, second information associated with the set of tasks as an augmented reality overlay. The device may perform an action related to the set of tasks, the ATM device, or the augmented reality overlay.