LiDAR Data Fusion Using Two-Stage Transformation Matrix Selection
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Solution Overview
Problem
Existing LiDAR systems face challenges in accurately fusing image information from multiple angles, resulting in incomplete and inaccurate three-dimensional image reconstruction.
Innovation Solution
A data fusion method and apparatus for a LiDAR system that involves obtaining point cloud data sets at different time points, determining candidate transformation matrix sets, selecting the most precise transformation matrix based on the second point cloud data set, and fusing point cloud data using the target transformation matrix to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If image information from multiple LiDARs at different angles is fused, then comprehensive three-dimensional image coverage is improved, but fusion accuracy deteriorates
Solution Approach 1:
The patent applies preliminary action by performing transformation matrix determination before actual data fusion. It pre-calculates multiple candidate transformation matrices based on the first point cloud data set, then selects the optimal one using the second point cloud data set. This preliminary preparation of transformation matrices enables accurate fusion of multi-angle LiDAR data while maintaining comprehensive three-dimensional coverage.
2Productivity
If transformation matrix is determined based on single time point data, then processing speed is improved, but transformation accuracy deteriorates
Solution Approach 1:
The patent uses preliminary action by determining candidate transformation matrices in advance based on the first point cloud data set. This pre-computation prepares multiple transformation options before the actual fusion process, enabling both high accuracy (through multiple candidates) and efficient processing (by avoiding real-time calculation during fusion).
Solution Approach 2:
The patent implements feedback by using the second point cloud data set to verify and select the optimal transformation matrix from the candidate set. The selection process compares transformed point cloud data against the second data set to determine which transformation matrix yields the best alignment, creating a feedback loop that ensures high accuracy while maintaining processing efficiency.
Data Source
AI summary
A data fusion method and apparatus for a LiDAR system includes a source LiDAR and at least one secondary LiDAR for obtaining a first point cloud data set of the LiDAR system at a first time point and a second point cloud data set of the system at a second time point separately; determining candidate transformation matrix sets based on the first point cloud data set, where each candidate transformation matrix set includes candidate transformation matrices for transforming point cloud data of a corresponding secondary LiDAR into a coordinate system of the source LiDAR; selecting a target transformation matrix from candidate transformation matrices in each of the candidate transformation matrix sets based on the second point cloud data set; and fusing point cloud data of the source LiDAR and point cloud data of the at least one secondary LiDAR based on a target transformation matrix corresponding to each secondary LiDAR.