Handheld Scanner Distance Sensing for Stable Vision Capture
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Solution Overview
Problem
Handheld barcode readers introduce challenges for machine vision applications due to their mobility, potential shift in position during scanning, and the risk of being held too close to the object, leading to difficulties in determining the optimal image capture range and inefficient battery usage.
Innovation Solution
Incorporation of a depth sensor, such as a time of flight (TOF) sensor, to determine the distance between the barcode reader and the scanned item, enabling precise image capture only when the object is within a predetermined range, thereby optimizing battery life and improving processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the imaging sensor continuously captures images for machine vision applications, then the object recognition capability is improved, but the battery consumption increases
Solution Approach 1:
The depth sensor performs preliminary distance measurement before the imaging sensor captures images. This preliminary action enables the system to determine in advance whether the object is within the optimal imaging range, thereby avoiding unnecessary image capture and reducing battery consumption while maintaining object recognition capability.
Solution Approach 2:
The depth sensor acts as an intermediary between the user and the imaging sensor. It provides distance information that mediates the decision-making process for image capture, allowing the system to intelligently control when the imaging sensor should be activated based on the object's distance from the scanner.
2Adaptability or versatility
If the imaging sensor captures images at all distances, then the versatility of the scanner is improved, but the image quality for machine vision deteriorates
Solution Approach 1:
The depth sensor performs preliminary distance measurement to determine whether the object is within the optimal imaging range before the imaging sensor captures images. This preliminary action ensures that images are only captured when the object is at the appropriate distance, maintaining high image quality for machine vision applications.
Solution Approach 2:
The system uses feedback from the depth sensor to control the imaging sensor. The distance information provided by the depth sensor feeds back to the control logic, which then determines whether to activate the imaging sensor, ensuring that images are captured only when quality conditions are met.
3Ease of operation
If the scanner operates in handheld mode with mobility, then the ease of operation is improved, but the stability of the scanning process deteriorates
Solution Approach 1:
The system transitions from a static distance assumption to a dynamic distance measurement approach. The depth sensor continuously measures the actual distance between the scanner and the object, allowing the system to adapt to the dynamic handheld scanning environment and maintain stable machine vision performance despite user movement.
Solution Approach 2:
The patent replaces the mechanical assumption of fixed distance with an optical measurement system (depth sensor). This substitution eliminates the need for users to maintain precise manual positioning, as the depth sensor automatically measures and compensates for distance variations caused by handheld movement.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The solution enhances the ability of handheld barcode readers to perform machine vision tasks by reducing unnecessary imaging, improving battery life, and increasing processing speed and computing power.
Implementation Method 1
a depth sensor operable to detect that an object in a range field of view (FOV) of the depth sensor is within a predetermined range from the handheld imaging device
Data Source
AI summary
Imaging devices, systems, and methods for capturing and processing images for vision applications in a non-fixed environment are described herein. An example device includes: a housing, a depth sensor operable to detect that an object in a range field of view is within a predetermined range from the device, an imaging sensor operable to capture images of an object, and one or more processors that: detect that the object is within the predetermined range in the range FOV; identify a first subset of images in a plurality of images captured by the imaging sensor for generating an identity for the object; and attempt to perform, using a second subset of images in the plurality of images, a decode event for an indicia associated with the object, the indicia visible in the imaging FOV for the second subset of images.


