Two-Phase Camera-LiDAR Alignment for Real-Time Vehicle Perception
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Determining camera to lidar alignment information in vehicles is computationally intensive, especially for real-time processing, which hinders efficient generation of virtual scene data from diverse image sources.
Innovation Solution
A method and system that process image and lidar data using machine learning for edge detection, clustering, and convex hull methods to generate alignment parameters, involving a joint analysis with three-dimensional cube spaces and transformation matrices to optimize alignment, based on conditions such as vehicle maneuvers and data point distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional alignment methods are used to determine camera to lidar alignment information, then alignment accuracy can be achieved, but computational resources and processing time are excessively high
Solution Approach 1:
The patent segments the alignment determination process into distinct phases: image data processing to extract vehicle contours and features, lidar data processing to identify corresponding points, and alignment parameter calculation. This segmentation allows each phase to be optimized independently, reducing overall computational burden while maintaining accuracy
Solution Approach 2:
The system performs preliminary processing of image and lidar data to pre-extract relevant features (vehicle contours, edges, corners) before the actual alignment calculation. This preliminary action prepares the data in advance, so that when alignment is needed, the computation is already significantly reduced
2Speed
If real-time processing is implemented for alignment determination, then processing speed improves, but computational resource requirements increase
Solution Approach 1:
The patent extracts only the essential features from image and lidar data that are necessary for alignment determination (vehicle contours, key points, edges). By taking out only the relevant information and discarding redundant data, the system achieves real-time processing with reduced computational resource consumption
Solution Approach 2:
The system performs partial processing on the data - focusing computation only on regions containing vehicles or relevant objects rather than processing the entire field of view. This selective partial action enables real-time performance with lower energy consumption
Data Source
AI summary
Systems and methods for generating alignment parameters for processing data associated with a vehicle. In one embodiment, a method includes: receiving image data associated with an environment of the vehicle; receiving lidar data associated with the environment of the vehicle; processing, by a processor, the image data to determine data points associated with at least one vehicle identified within image data; processing, by the processor, the lidar data to determine data points associated with at least one vehicle identified within the lidar data; selectively storing the data points in a data buffer based on at least one condition associated with a quality of the data points; processing, by the processor, the data points in the data buffer with a joint analysis method to generate alignment parameters between the lidar and the camera; and processing future data based on the alignment parameters.