3D-2D Data Registration via Motion Compensation
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Solution Overview
Problem
The challenge in robotics and machine vision is aligning three-dimensional (3D) data with two-dimensional (2D) image data, particularly due to the 'capture-timing gap' issue where data is not captured simultaneously and parallax errors from different vantage points, making it difficult to match and register the data accurately.
Innovation Solution
A system and method that utilize sensors and cameras to capture 3D and 2D data, with processors identifying 3D segments and 2D boundary information, and adjusting their positions based on object speed and heading to align them in 3D space, using techniques like time-shifting and shape matching to compensate for capture time differences and parallax errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of moving object
If 3D data and 2D image data are captured at different times, then the system can process data from multiple time points, but the data cannot be accurately aligned due to object motion and parallax errors
Solution Approach 1:
The system performs preliminary actions by capturing both 3D data and 2D image data at multiple time points before final registration is needed. This allows the system to have advance knowledge of object positions and motions, enabling more accurate registration by predicting where objects will be at the reference time rather than attempting to align data captured at different moments directly.
Solution Approach 2:
The system dynamically adjusts the registration process by estimating object velocities and positions at different time points. Instead of using static registration methods, the system models object motion dynamics to compensate for the time gap between captures, calculating corrected positions based on estimated velocities and time differences.
2Adaptability or versatility
If 3D data and 2D image data are captured from different vantage points, then the system can gather data from multiple sensor locations, but parallax errors make accurate spatial alignment difficult
Solution Approach 1:
The system performs preliminary camera calibration and intrinsic parameter estimation for both 3D and 2D sensors before the actual registration task. This preliminary setup establishes the geometric relationships and projection models needed to compensate for different vantage points, enabling accurate alignment despite spatial separation of sensors.
Solution Approach 2:
The system introduces an intermediary transformation model that mediates between the 3D and 2D coordinate systems. This intermediary layer accounts for the different vantage points by modeling the geometric relationship between sensors, using camera matrices and projection transforms to bridge the spatial gap and eliminate parallax errors during registration.
3Ease of manufacture
If the system processes 3D and 2D data separately, then each data type can be processed independently, but the registration step becomes more complex and time-consuming
Solution Approach 1:
The system segments the data processing into distinct independent stages: 3D data processing, 2D image processing, and registration. Each stage can be processed independently using specialized algorithms, then the results are combined through the registration module. This segmentation maintains processing independence while managing complexity through modular architecture.
Solution Approach 2:
The registration process incorporates feedback mechanisms where the system continuously adjusts registration parameters based on the quality of alignment between 3D and 2D data. This feedback loop automatically optimizes the registration transformation, reducing manual intervention and simplifying the overall process despite the complexity of coordinating multiple data types.
Data Source
AI summary
Systems and methods described herein relate to registering three-dimensional (3D) data with two-dimensional (2D) image data. One embodiment receives 3D data from one or more sensors and 2D image data from one or more cameras; identifies a 3D segment in the 3D data and associates it with an object; identifies 2D boundary information for the object; determines a speed and a heading for the object; and registers the 3D segment with the 2D boundary information by adjusting the relative positions of the 3D segment and the 2D boundary information based on the speed and heading of the object and matching, in 3D space, the 3D segment with projected 2D boundary information.