Image-to-World Coordinate Transformation for Autonomous Vehicle Ground Truth
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Solution Overview
Problem
Conventional methods for generating ground truth labels for autonomous vehicle training are time-consuming and require substantial human interaction, often skipping frames to reduce burden, leading to reduced quality and quantity of labeled data, and involve computationally expensive processes to derive 3D world space positions from 2D images.
Innovation Solution
A system that projects image space coordinates to 3D vehicle space coordinates and then transforms them to 3D world space coordinates using known vehicle positions and orientations, enabling efficient generation of ground truth data by correlating temporal vehicle trajectories with known world space coordinates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional human labeling approach is used, then ground truth labels can be generated, but the process is time-consuming and requires substantial human interaction
Solution Approach 1:
The system performs preliminary action by automatically generating candidate lane line labels using image processing algorithms before human review. This pre-labeling step reduces the time required for human operators to create ground truth data, as they only need to verify and correct rather than create labels from scratch.
Solution Approach 2:
The system introduces an intermediary automated labeling system that sits between the raw image data and final ground truth labels. This intermediary performs initial label generation using computer vision techniques, reducing the direct human interaction time while maintaining label quality through subsequent verification steps.
2Productivity
If frames are skipped to reduce labeling burden, then labeling time decreases, but the quantity and quality of labeled ground truth data is reduced
Solution Approach 1:
The system enables self-service labeling by implementing automated algorithms that can generate lane line labels without continuous human intervention. The automated system processes frames independently, allowing all frames to be labeled rather than skipping some, thereby maintaining both high productivity and complete data coverage.
Solution Approach 2:
The system replaces the mechanical human labeling process with automated computer vision algorithms. This substitution eliminates the need to skip frames due to human fatigue or time constraints, allowing continuous processing of all video frames while maintaining consistent labeling quality throughout the dataset.
3Measurement precision
If 3D world space positions are derived from 2D image coordinates using conventional methods, then ground truth labels include 3D positions, but the process requires computationally expensive per-frame processing of LIDAR data
Solution Approach 1:
The system merges the 2D lane line detection process with 3D position calculation by integrating camera extrinsic parameter transformations. Instead of separately processing LIDAR data and image data, the system combines these processes, using the already-detected 2D lane lines and applying geometric transformations to directly obtain 3D positions, thereby reducing computational overhead.
Solution Approach 2:
The system changes the parameters used for 3D position derivation by utilizing camera extrinsic parameters (rotation and translation matrices) instead of requiring full LIDAR point cloud processing. This parameter transformation approach maintains 3D position accuracy while significantly reducing the computational energy required per frame.
Data Source
AI summary
In various examples, image space coordinates of an image from a video may be labeled, projected to determine 3D vehicle space coordinates, then transformed to 3D world space coordinates using known 3D world space coordinates and relative positioning between the coordinate spaces. For example, 3D vehicle space coordinates may be temporally correlated with known 3D world space coordinates measured while capturing the video. The known 3D world space coordinates and known relative positioning between the coordinate spaces may be used to offset or otherwise define a transform for the 3D vehicle space coordinates to world space. Resultant 3D world space coordinates may be used for one or more labeled frames to generate ground truth data. For example, 3D world space coordinates for left and right lane lines from multiple frames may be used to define lane lines for any given frame.