Point Cloud Positioning Error Detection via Registration Quality
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Solution Overview
Problem
Current point cloud positioning systems for self-driving cars and intelligent driving assistance, relying on LiDAR sensing data and iterative point cloud overlay, are prone to errors due to poor initial estimates, leading to inaccurate calculations.
Innovation Solution
A point cloud positioning error detection method and system that registers first point data with a target point cloud map, selects data based on a default distance, calculates matching scores and step vectors, and outputs localization fault detection results based on the intersection of these scores and values, effectively identifying and correcting registration errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If point cloud overlay is carried out through iteration using local fractional gradient, then positioning efficiency is improved, but positioning accuracy deteriorates when initial estimate is poor
Solution Approach 1:
The patent applies preliminary action by performing a coarse registration using global point cloud features (such as plane detection and feature matching) before the iterative local gradient optimization. This preliminary registration provides a better initial estimate for the iterative process, preventing convergence to local minima while maintaining the efficiency benefits of iterative optimization.
Solution Approach 2:
The patent segments the registration process into two distinct stages: a global registration stage that establishes a rough alignment using overall point cloud characteristics, and a local refinement stage that uses iterative gradient-based optimization. This segmentation allows each stage to focus on specific aspects of the problem, improving both accuracy and efficiency.
2Speed
If reliance is placed on local gradients for iterative optimization, then convergence speed is improved, but error detection capability deteriorates
Solution Approach 1:
The patent implements feedback mechanisms by continuously monitoring registration quality metrics during the iterative process, such as point-to-point distance errors and point-to-plane distances. When the improvement between iterations falls below a threshold or errors increase, the system can detect potential failures and terminate or correct the registration process.
Solution Approach 2:
The patent applies preliminary anti-action by implementing validation checks and error detection mechanisms before and during the iterative optimization process. These checks include verifying that the initial registration quality meets minimum thresholds and monitoring for divergence or local minimum convergence, allowing the system to prevent erroneous results before they occur.
Data Source
AI summary
A point cloud positioning error detection method, performed by a processing device, includes: obtaining a plurality of pieces of first point data and a target point cloud map, wherein the target point cloud map includes a plurality of pieces of target point data, registering the first point data and the target point data to obtain a plurality of pieces of second point data, selecting a plurality of pieces of third point data from the second point data according to a first default distance, calculating a plurality of matching scores of the third point data relative to the target point data, obtaining a plurality of step vectors corresponding to the third point data, respectively, when said registering converges, and obtaining a plurality of effective values according to directions of the step vectors, and outputting a localization fault detection result based on an intersection of the matching scores and the effective values.