Camera Orientation Estimation Using Multi-Frame Ground Alignment
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Solution Overview
Problem
Existing methods for estimating camera orientation relative to a ground surface, particularly in machine vision applications, face challenges such as high uncertainty in ground plane estimation and inadequate accuracy due to reliance on vertical vanishing points, which are not always available in captured images, especially for vehicles like AGVs undergoing movements and changes in orientation.
Innovation Solution
A method that combines ground plane estimation results from multiple sequential video frames or images, using orthogonal direction classification and maximum a-posteriori camera orientation estimation to achieve high accuracy, even when vertical structures are absent, by classifying and grouping line segments into orthogonal directional groups and iteratively refining camera orientation uncertainty.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ground plane estimation is performed using vertical vanishing points from a single image, then the estimation process is simple and fast, but the precision and reliability are insufficient when vertical structures are absent or uncertainty is high
Solution Approach 1:
The patent combines ground plane estimation results from multiple sequential video frames by merging the information through probabilistic fusion. This integration of multiple observations increases measurement precision while managing the complexity through systematic combination rules rather than requiring complex individual estimations.
Solution Approach 2:
The patent changes the parameter representation from fixed single-frame estimates to probabilistic distributions that evolve over time. By representing camera orientation as a probability distribution that updates with each frame, the system achieves higher precision through temporal accumulation while maintaining computational tractability.
2Measurement precision
If manual calibration procedure is used for camera orientation, then initial accuracy can be achieved, but it is troublesome and error-prone for fleet vehicles and subject to drift over time
Solution Approach 1:
The patent implements self-service by enabling vehicles to automatically recalibrate their camera orientation using onboard sensors and visual data from the environment. The system performs autonomous orientation estimation by detecting line segments and vanishing points, eliminating the need for manual calibration operations while maintaining accuracy and correcting drift automatically.
Solution Approach 2:
The patent employs feedback mechanisms where the estimated camera orientation is continuously refined based on observed geometric structures in the environment. The system uses detected line segments and orthogonal relationships as feedback to correct orientation estimates, enabling automatic drift compensation without manual intervention.
3Adaptability or versatility
If vertical vanishing points are used for orientation estimation, then the method is straightforward when available, but it becomes impossible to estimate ground plane when no vertical structure is present
Solution Approach 1:
The patent achieves universality by developing an estimation method that works with multiple types of geometric structures, not just vertical lines. The system can detect and utilize orthogonal relationships from various scene elements including horizontal and diagonal lines, making the method adaptable to diverse environments while maintaining reliable estimation through multiple potential feature sources.
Data Source
AI summary
An iterative multi-image camera orientation estimation comprising: capturing an image of a scene before the camera; detecting line segments in the scene; computing a maximum likelihood (ML) camera orientation by maximizing a likelihood objective by rotating the camera's X-Y-Z coordinate system such that it is being optimally aligned with the line segments in at least two of the frontal, the lateral, and the vertical orthogonal directions; estimating a maximum a-posteriori (MAP) camera orientation that maximizes an a-posteriori objective such that the MAP camera orientation is an optimal value in between the priori camera orientation and the ML camera orientation, and is closer to the one with smaller uncertainty; iterating the multi-image camera orientation estimation with the priori camera orientation and its corresponding priori camera orientation uncertainty set to the computed MAP camera orientation and its corresponding uncertainty respectively until the uncertainty is lower than a threshold.