Vehicle Camera Pose Estimation Using Vanishing Point Accumulation
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Solution Overview
Problem
The existing methods for estimating the pose of a camera in vehicles during driving are inaccurate and unreliable, leading to poor quality around-view images due to errors in distance measurement to objects, as the camera pose is not precisely estimated.
Innovation Solution
A control method that accumulates vanishing point candidates from images input by the camera, generates a vanishing point accumulation map, and estimates the camera pose using principal component analysis to determine the roll, yaw, and pitch angles, allowing for accurate compensation of camera tolerance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If camera pose is estimated using traditional methods during driving, then the estimation process is simple, but the estimation accuracy and reliability are poor
Solution Approach 1:
The system performs preliminary actions by accumulating vanishing point candidates from multiple images before final pose estimation. This pre-accumulation process prepares data in advance, improving estimation accuracy by having sufficient vanishing point information ready when pose estimation is needed, rather than relying on single-image estimates.
Solution Approach 2:
The patent transitions from two-dimensional image coordinates to three-dimensional camera pose parameters (roll, pitch, yaw angles). By accumulating vanishing points in 2D image space and then transforming this accumulated information into 3D pose space using principal component analysis, the system achieves more accurate and reliable pose estimation.
2Reliability
If camera pose estimation is performed using single-image methods, then the processing time is short, but the reliability and robustness to noise are poor
Solution Approach 1:
The system merges multiple vanishing point candidates from different images into a single accumulation map. By combining information from multiple sources (multiple images and their vanishing points) into one consolidated data structure, the system improves reliability and noise robustness while managing processing time through efficient accumulation.
Solution Approach 2:
The vanishing point accumulation process operates continuously as new images are captured during driving. Rather than performing discrete, time-consuming estimations, the system continuously accumulates vanishing point information, maintaining an ever-improving estimate that becomes increasingly reliable over time without significant additional processing delays.
3Measurement precision
If vanishing point candidates are accumulated from multiple images, then the pose estimation becomes more accurate, but the data processing complexity increases
Solution Approach 1:
The vanishing point accumulation map serves as an intermediary data structure between raw image inputs and final pose estimation. This intermediate representation consolidates vanishing point information from multiple images into a single organized map, simplifying the subsequent pose estimation process and making the system more manageable despite handling multiple images.
Solution Approach 2:
The system changes parameters by transforming vanishing point coordinates from image space to pose space through principal component analysis. This parameter transformation converts 2D image coordinates into 3D camera orientation parameters (roll, pitch, yaw), enabling accurate distance measurement while managing complexity through mathematical transformation rather than complex geometric reasoning.
Data Source
AI summary
A control method of a vehicle includes: detecting straight lines in an image input from a camera; generating a vanishing point candidate based on an intersection of the straight lines; accumulating the vanishing point candidate; generating a vanishing point accumulation map based on the accumulated vanishing point candidates; estimating a pose of the camera based on vanishing point cluster information of the vanishing point accumulation map; and adjusting a distance to an object in the image based on the pose of the camera.