Camera-LiDAR Pole Alignment for Real-Time Vehicle Perception
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Solution Overview
Problem
Determining camera to lidar alignment information in vehicles is computationally intensive, especially for real-time processing, which hinders efficient image data merging and environmental perception.
Innovation Solution
A method and system that process image and lidar data to identify pole data points, form data point pairs, iteratively determine a transformation matrix through perturbations, and generate alignment parameters, using cross-correlation and clustering techniques to produce a binary output and filter data based on proximity and geometrical conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional alignment methods are used to determine camera to lidar alignment information, then alignment accuracy is achieved, but computational complexity increases and real-time processing becomes difficult
Solution Approach 1:
The patent segments the alignment problem by focusing only on pole detection and matching between camera and lidar data, rather than processing all environmental features. This selective approach reduces computational complexity while maintaining alignment accuracy through targeted feature extraction using 2D filters for pole identification in camera images and corresponding pole detection in lidar point clouds.
Solution Approach 2:
The patent applies preliminary action by pre-processing camera images with 2D filters to detect poles before alignment computation, and pre-processing lidar data to identify pole points. This preliminary feature extraction reduces the data volume requiring complex alignment calculations, thereby reducing computational complexity while preserving alignment precision.
2Measurement precision
If conventional alignment methods are used to determine camera to lidar alignment information, then alignment accuracy is achieved, but processing time increases
Solution Approach 1:
By segmenting the alignment task to focus specifically on pole features rather than all scene elements, the patent reduces the amount of data requiring processing. This selective segmentation maintains alignment accuracy through reliable pole feature matching while significantly reducing processing time through reduced computational scope.
Solution Approach 2:
The patent performs preliminary detection of poles in both camera and lidar data before conducting alignment computations. This pre-processing step identifies only the relevant features needed for alignment, reducing the time required for subsequent alignment calculations while maintaining accuracy through focused feature matching.
3Measurement precision
If comprehensive image and lidar data processing is performed to ensure accurate alignment, then measurement precision improves, but energy consumption increases
Solution Approach 1:
The patent segments the data processing workload by focusing exclusively on pole feature extraction and matching between camera and lidar inputs. This segmentation reduces the total computational energy required while maintaining alignment precision, as pole features provide sufficient geometric constraints for accurate alignment without requiring processing of all environmental features.
Solution Approach 2:
By performing preliminary pole detection in both camera and lidar data before alignment computation, the patent reduces the energy-intensive alignment calculation workload. This preliminary filtering identifies only the necessary features for alignment, reducing overall energy consumption while preserving measurement precision through focused feature matching.
Data Source
AI summary
Systems and methods are provided 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 pole identified within image data; processing, by the processor, the lidar data to determine data points associated with at least one pole identified within the lidar data; selectively storing the data points as data point pairs in a data buffer; iteratively processing, by the processor, the data point pairs with a plurality of perturbations to determine a transformation matrix; generating, by the processor, alignment data based on the transformation matrix; and processing future data based on the alignment parameters.