Multi-Channel LiDAR Interference Point Determination Method
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Solution Overview
Problem
Multi-channel LiDAR systems face challenges in effectively determining interference points caused by signal crosstalk, which affects ranging accuracy and driving safety.
Innovation Solution
The method involves obtaining a target point cloud from a highly reflective object within a specific distance range and determining suspected interference points in other channels based on distance and reflectivity values. These suspected points are then verified using variance analysis or interference point range determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multi-channel LiDAR is used to expand field of view, then scanning efficiency is improved, but signal crosstalk causes interference points that reduce ranging accuracy
Solution Approach 1:
The patent extracts and removes interference points from the point cloud data by identifying them through distance threshold comparison and reflectivity analysis. Interference points are separated from valid measurement points and eliminated from the final output, thereby resolving the crosstalk issue while maintaining the multi-channel scanning capability.
Solution Approach 2:
The patent introduces an intermediary processing mechanism that analyzes the relationship between point cloud distances and reflectivity values to identify and filter interference points. This intermediary analysis layer enables the system to distinguish between valid reflections and crosstalk-induced interference, maintaining measurement precision while preserving the expanded field of view.
2Measurement precision
If interference point determination is performed, then ranging accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent changes the parameters for identifying interference points by using distance threshold comparison and reflectivity value analysis. Instead of complex multi-variable optimization, the system uses simple parameter thresholds (distance < threshold, reflectivity < threshold) to efficiently filter interference points, reducing computational complexity while maintaining accuracy.
Solution Approach 2:
The patent applies partial action by only performing interference point determination on point cloud data that meets specific criteria (e.g., points within a certain distance range or with abnormal reflectivity). This selective processing reduces the overall computational burden compared to analyzing every point uniformly, while still achieving effective interference filtering.
Data Source
AI summary
An interference point determining method is provided. The method includes: obtaining a target point cloud corresponding to a highly reflective object from a target channel; obtaining a to-be-determined point cloud at the same pixel position as the target point cloud from each channel other than the target channel based on the target point cloud; based on a distance value and a reflectivity of each to-be-determined point cloud, and distance values and reflectivities respectively corresponding, to other point clouds in a neighborhood of each to-be-determined point cloud, determining whether each to-be-determined point cloud is a suspected interference point; and based on a variance between distance values of the other point clouds in the neighborhood of one to-be-determined point cloud and the one to-be-determined point cloud, or based on an interference point range determined for the one to-be-determined point cloud, determining whether the one to-be-determined point cloud is the interference point.