Point-to-Point Image Translation for Autonomous Mapping
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Solution Overview
Problem
Current methods for generating and updating three-dimensional maps, especially for autonomous and semi-autonomous vehicle control, are resource-intensive and time-consuming due to the need for accurate feature detection and correlation of features between images from different perspectives, which can lead to safety concerns and inefficiencies.
Innovation Solution
A method that involves receiving three-dimensional coordinates from one image, projecting them onto an approximate ground plane, and generating translation vectors from corresponding points in another image captured from a different perspective, allowing for precise location and orientation of objects and features across varying viewpoints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used for 3D modelling and feature detection, then measurement accuracy can be maintained, but the process becomes extremely time-consuming and resource-intensive
Solution Approach 1:
The patent replaces traditional manual or mechanical 3D modelling methods with a computational approach that uses image correspondence and projection geometry. Instead of manual measurement and calculation, the system automatically computes 3D coordinates by projecting 2D image points through known camera parameters and geometric relationships, dramatically reducing time while maintaining accuracy
Solution Approach 2:
The patent creates a computational model that copies the geometric relationships between images and 3D space. By establishing correspondences between multiple 2D images and using projection mathematics, the system reproduces accurate 3D coordinates without requiring physical measurement, thus speeding up the process while preserving measurement precision
2Measurement precision
If manual or semi-automated analysis is used for large amounts of data, then accuracy can be maintained, but the method is not practical for modern applications requiring quick processing
Solution Approach 1:
The patent replaces manual analysis with an automated computational system that uses image processing algorithms. The system automatically detects features in 2D images, establishes correspondences between multiple views, and computes 3D coordinates through mathematical projection, enabling high-speed processing of large datasets while maintaining the accuracy that previously required human analysis
Solution Approach 2:
The patent transforms the problem from manual feature-by-feature analysis to a parameter-driven computational approach. By using camera parameters, projection geometry, and image correspondence parameters, the system automatically processes large amounts of data through mathematical transformations, achieving both high productivity and maintained precision
3Ease of operation
If feature detection methods are used for terrain and object detection, then object identification can be achieved, but reliability cannot be guaranteed leading to safety concerns
Solution Approach 1:
The patent replaces unreliable feature detection methods with a geometric projection-based system. Instead of detecting features and hoping for accurate results, the system uses known camera parameters and established image correspondences to compute 3D coordinates through mathematical projection, providing guaranteed reliability based on geometric principles rather than heuristic detection
Solution Approach 2:
The patent introduces image correspondences and projection geometry as intermediaries between 2D images and 3D object locations. Rather than directly detecting objects and trusting the results, the system uses the intermediary mathematical relationships of projection to translate 2D image points into accurate 3D coordinates, ensuring reliability through geometric invariance
Data Source
AI summary
A method, apparatus and computer program product are provided for establishing correspondences between images using through generation of a translation between the different perspectives. Methods may include: receiving first sensor data from a first image sensor, where the first sensor data includes a first image of an environment captured from a first perspective; receiving second sensor data from a second image sensor of a second image of the environment captured from a second perspective; identifying image correspondence points between the first sensor data and the second sensor data; computing pairwise vectors between corresponding pairs of first projected points of the first sensor data and second projected points of the second sensor data; clustering the pairwise vectors according to magnitude and orientation; and generating a translation vector from clusters of pairwise vectors, where the translation vector represents a shift of the second image sensor data to correspond to ground truth data.