Stereo Vision Image Density Compensation for Obstacle Detection
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Solution Overview
Problem
Stereo vision guidance systems face challenges in processing 3D vision data in real-time for vehicular navigation due to stereo mismatches, lack of texture information, and inadequate illumination, making accurate and timely imaging processing burdensome.
Innovation Solution
A method and system that process stereo vision data using image density, where an estimator calculates a reference 3D image density rating and an image density compensator adjusts for changes in image density between reference and evaluated volumetric cells, enabling obstacle detection by identifying cells that meet or exceed a minimum density rating.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If stereo vision data is collected for 3D navigation, then navigation capability is improved, but processing complexity and computational burden increase
Solution Approach 1:
The patent extracts and processes only the essential density information from the complex stereo vision data, separating the useful navigational information (density ratings) from the redundant visual data. This allows navigation capability to be improved while reducing processing complexity by focusing computation only on density estimation and compensation rather than full stereo processing.
Solution Approach 2:
The patent transforms the complex stereo vision data into a simplified parameter representation (density rating) that captures the essential navigational information. By changing the data representation from raw stereo images to compensated density ratings, the system reduces computational burden while maintaining navigation capability.
2Speed
If real-time processing is implemented for vehicle guidance, then response time is improved, but processing accuracy deteriorates due to computational limitations
Solution Approach 1:
The patent performs preliminary density estimation and compensation calculations during the data collection phase, preparing processed density ratings in advance. This preliminary processing enables real-time vehicle guidance decisions while maintaining accuracy, as the complex computations are completed before the actual navigation decision is needed.
Solution Approach 2:
The system uses the density information itself to guide the processing, where the estimated density ratings automatically determine which areas require attention and how compensation should be applied. This self-service approach optimizes processing accuracy for real-time applications by adaptively allocating computational resources based on the density distribution of the scene.
3Measurement precision
If image density compensation is applied, then obstacle detection accuracy is improved, but processing time increases
Solution Approach 1:
The patent applies density compensation locally to specific volumetric cells and regions rather than processing the entire image uniformly. By targeting only the areas where density variation is significant and applying compensation selectively, the system improves obstacle detection accuracy in critical regions while minimizing overall processing time.
Solution Approach 2:
The system applies compensation to the extent necessary to achieve accurate obstacle detection, avoiding over-processing. By applying density compensation only to the degree needed for reliable obstacle identification (rather than maximum possible compensation), the system balances accuracy improvement with processing time constraints.
Data Source
AI summary
An image collection system collects stereo image data comprising left image data and right image data of a particular scene. An estimator estimates a reference three dimensional image density rating for corresponding reference volumetric cell within the particular scene at a reference range from an imaging unit. An image density compensator compensates for the change in image density rating between the reference volumetric cell and an evaluated volumetric cell by using a compensated three dimensional image density rating based on a range or relative displacement between the reference volumetric cell and the evaluated volumetric cell. An obstacle detector identifies a presence or absence of an obstacle in a particular compensated volumetric cell in the particular scene if the particular compensated volumetric cell meets or exceeds a minimum three dimensional image density rating