Mono Camera 3D Coordinate Estimation for Autonomous Driving
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Solution Overview
Problem
Current autonomous driving methods require expensive and complex equipment like high-precision GPS receivers and stereo cameras to estimate three-dimensional coordinates for real-time information, which increases costs and reduces reliability.
Innovation Solution
A method using a mono camera with a pinhole camera model and linear interpolation to estimate three-dimensional coordinate values for each pixel, allowing for real-time estimation of object locations and semantic information without the need for high-precision GPS or stereo cameras, by inputting camera height and setting reference values, and applying lens distortion correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a high-precision GPS receiver is used to reduce location error range, then measurement precision is improved, but device cost increases
Solution Approach 1:
The patent replaces expensive high-precision GPS receivers with multiple low-cost GPS receivers. By using cheaper components in a multi-unit configuration, the system achieves the required measurement precision through data fusion rather than relying on a single expensive device.
Solution Approach 2:
The patent combines data from multiple low-cost GPS receivers to achieve the precision of a single high-precision receiver. By merging multiple data sources and processing them together, the system compensates for individual device limitations and achieves the required location accuracy.
2Measurement precision
If multiple GPS receivers are used to complement location information, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent divides the positioning function into multiple independent GPS receivers, each operating separately. By segmenting the system into independent units that can be configured and processed individually, the overall complexity is managed through modular architecture rather than a single complex integrated system.
Solution Approach 2:
The patent uses multiple GPS receivers that perform the same function independently. Each receiver is a universal, standardized component that can be independently configured and replaced, simplifying the overall system architecture compared to a custom integrated solution.
3Measurement precision
If multiple GPS receivers are interconnected to process data, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent uses multiple copies of the same GPS receiver hardware and software processing logic. Each receiver independently processes data using the same algorithms, and the results are combined. This copying approach simplifies the system by using identical, well-tested components rather than developing complex custom processing logic.
4Measurement precision
If a stereo camera is used to adjust depth measurement area, then measurement precision is improved, but device cost increases
Solution Approach 1:
The patent replaces expensive stereo camera systems with a single mono camera. By using a cheaper mono camera combined with computational methods, the system achieves depth measurement capability without the high cost of stereo hardware.
5Measurement precision
If image-processed data amount is increased to improve accuracy, then measurement precision is improved, but processing time increases
Solution Approach 1:
The patent processes only the necessary portion of image data required for depth estimation rather than performing exhaustive processing on all image pixels. By selectively processing only relevant data regions and using mathematical models to estimate depth for remaining areas, the system achieves acceptable accuracy with reduced processing time.
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 efficiently acquires reliable autonomous driving information in real-time with reduced data processing time and increased accuracy, minimizing the need for expensive equipment and reducing errors associated with moving LiDAR data.
Implementation Method 1
using modeling by a pinhole camera model and linear interpolation
Implementation Method 2
using modeling by a pinhole camera model and linear interpolation
Implementation Method 3
applying lens distortion correction
Data Source
AI summary
Proposed are a method of estimating a three-dimensional coordinate value for each pixel of a two-dimensional image, and a method of estimating autonomous driving information using the same, and more specifically, a method that can efficiently acquire information needed for autonomous driving using a mono camera. This method is able to acquire information having sufficient reliability in real-time without using expensive equipment such as a high-precision GPS receiver, a stereo camera or the like required for autonomous driving.