Optical Flow Sensor Using Directional-Invariant Filtering
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Solution Overview
Problem
Conventional optical flow sensing systems for devices like optical mice, remote controllers, and unmanned aerial vehicles require multiple image sensors, increasing hardware costs and complexity, and do not effectively utilize ambient light for navigation.
Innovation Solution
An optical flow sensing method and system using a single image sensor with a directional-invariant filter and global shutter, capable of processing images to estimate motion vectors by comparing filtered block images, reducing the need for multiple sensors and leveraging ambient light.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple image sensors are used in conventional optical flow sensor systems, then measurement precision is improved, but device complexity and hardware cost increase
Solution Approach 1:
The patent divides the image processing into blocks and applies directional-invariant filtering to each block separately. This segmentation allows a single image sensor to process different regions with appropriate filtering, achieving multi-sensor level precision through single-sensor block-based processing
Solution Approach 2:
The patent changes the processing parameters by applying directional-invariant filters that adapt to different block characteristics. By modifying filter parameters based on local image properties rather than using multiple fixed sensors, the system achieves precise motion detection with reduced hardware complexity
2Measurement precision
If multiple image sensors are deployed to obtain six-axis data, then measurement precision is improved, but manufacturing cost increases
Solution Approach 1:
The patent makes a single image sensor perform multiple functions by applying different directional-invariant filters to different blocks. This universal approach allows one sensor to replace multiple specialized sensors, reducing manufacturing costs while maintaining six-axis data acquisition capability
Solution Approach 2:
The patent adds the dimension of directional-invariant filtering processing to compensate for the reduction in sensor quantity. By introducing sophisticated 2D block-based filtering and correlation analysis, the system achieves multi-sensor measurement precision with single-sensor hardware
3Ease of operation
If conventional optical mouse navigation algorithm is used without filters, then ease of operation is maintained, but measurement precision deteriorates
Solution Approach 1:
The patent applies directional-invariant filtering as a preliminary step before motion calculation. By pre-processing image blocks with appropriate filters to enhance features, the system maintains simple navigation operation while significantly improving feature detection precision
Solution Approach 2:
The patent introduces directional-invariant filters as intermediary processing elements between image capture and motion calculation. These filters mediate the transformation by enhancing relevant features while preserving the simplicity of the overall navigation operation
4Measurement precision
If external sensing bar is added to remote controller, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent enables the remote controller to perform its own motion detection using an integrated optical flow sensor with directional-invariant filtering. This self-service capability eliminates the need for external sensing bars, maintaining measurement precision while reducing device complexity
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 allows for precise motion estimation and reduced hardware costs by using only two image sensors to obtain six-axis data, eliminating the need for external sensing bars in remote controllers and enhancing the efficiency of devices like unmanned aerial vehicles.
Implementation Method 1
using a first directional-invariant filter device upon at least one first block of the first image to process pixel values of the at least one first block of the first image, to generate a first filtered block image
Implementation Method 2
using a first image sensor to capture a first image and a second image
Data Source
AI summary
An optical flow sensing method includes: using an image sensor to capture images; using a directional-invariant filter device upon at least one first block of the first image to process values of pixels of the at least one first block of the first image, to generate a first filtered block image; using the first directional-invariant filter device upon at least one first block of the second image to process values of pixels of the at least one first block of the second image, to generate a second filtered block image; comparing the filtered block images to calculate a correlation result; and estimating a motion vector according to a plurality of correlation results.