3D Depth Sensor Auto-Focus for Indicia Scanning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional mobile device autofocus methods for scanning indicia are time-consuming and inefficient, especially under poor lighting conditions and with excessive motion, due to their reliance on sweeping focal distances and analyzing intensity differences across a wide range.
Innovation Solution
A three-dimensional depth sensor-based auto-focus system that predicts optimal focal distances and adjusts the autofocusing lens assembly progressively, capturing images at these distances to quickly identify and decode indicia, while also optimizing exposure levels for improved image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional autofocus routine sweeps across wide range of focal distances, then proper focal distance can be determined, but focusing time increases significantly
Solution Approach 1:
The depth sensor captures depth information of the scene before the autofocus routine begins. This preliminary depth data is used to predict optimal focal distances, allowing the system to skip unnecessary focal distance sweeps and directly focus on predicted distances, significantly reducing focusing time while maintaining accuracy
Solution Approach 2:
The system replaces the traditional mechanical focal distance sweeping method with a prediction-based approach using depth sensor data and machine learning algorithms. Instead of physically sweeping through multiple focal positions and evaluating intensity differences, the system uses computational prediction to determine the optimal focal distance directly
2Measurement precision
If autofocus routine evaluates intensity differences across wide focal range, then proper focus can be achieved, but power consumption increases
Solution Approach 1:
Depth information is captured in advance before the autofocus process begins. This preliminary data enables the system to predict optimal focal distances without performing extensive intensity difference evaluations across multiple focal positions, thereby reducing computational power consumption while maintaining focus accuracy
Solution Approach 2:
The system extracts depth information from the scene using a depth sensor before the autofocus routine. This extracted depth data is then used to predict focal distances, eliminating the need for energy-intensive intensity difference calculations across the full focal range
3Measurement precision
If lens assembly adjusts focal distance across wide range, then proper focus is achieved, but wear on lens assembly increases
Solution Approach 1:
The depth sensor captures scene depth information before autofocus begins, enabling prediction of optimal focal distances. This allows the lens assembly to make direct adjustments to predicted focal positions rather than sweeping through the entire focal range, reducing mechanical wear and extending lens assembly lifespan
Solution Approach 2:
The system skips unnecessary focal distance positions by using depth-based prediction to identify likely optimal focal distances. The lens assembly jumps directly to predicted focal positions rather than methodically testing each position in the range, reducing the number of mechanical adjustments and minimizing wear
4Measurement precision
If traditional autofocus is used under poor lighting conditions, then focus can be achieved, but motion blur increases due to excessive focusing time
Solution Approach 1:
Depth information is captured in advance before the autofocus process begins. This preliminary depth data enables rapid prediction of optimal focal distances, allowing the system to complete focusing quickly even under poor lighting conditions, thereby minimizing the window for motion blur to occur
Solution Approach 2:
The system replaces slow mechanical focal sweeping with rapid computational prediction based on depth sensor data. This substitution enables the system to determine optimal focus quickly without the time penalty of traditional methods, reducing motion blur in challenging lighting conditions
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
This approach significantly reduces focusing time, power consumption, and wear on the lens assembly, enabling faster detection and decoding of indicia with reduced motion blur and improved accuracy under varying conditions.
Implementation Method 1
capturing a first depth image of a field of view with the three dimensional depth sensor; determining distances from the indicia reading terminal to major surfaces in the depth image
Implementation Method 2
an autofocusing lens assembly positioned proximate to the two dimensional image sensor such that the incident light passes through the autofocusing lens before reaching the two dimensional image sensor
Data Source
Figure 1A~1B
Figure 2
Figure 3
AI summary
An indicia reading terminal has a three-dimensional depth sensor, a two dimensional image sensor, an autofocus lens assembly, and a processor. The three dimensional depth sensor captures a depth image of a field of view and create a depth map from the depth image, the depth map having one or more surface distances. The two dimensional image sensor receives incident light and capture an image therefrom. The autofocusing lens assembly is positioned proximate to the two dimensional image sensor such that the incident light passes through the autofocusing lens before reaching the two dimensional image sensor. The processor is communicatively coupled to the two dimensional image sensor, the three dimensional depth sensor, and the autofocusing lens assembly.