LiDAR Point Cloud Data Reduction with Reconstruction
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Solution Overview
Problem
LiDAR systems face challenges in reducing data without significant information loss, particularly in scenarios like rain or vegetation detection, where bandwidth limitations cause important reflections from small, poorly reflecting objects to be missed during data transmission.
Innovation Solution
A method that compares points in a point cloud for similarity and only transmits points exceeding a threshold, generating additional information for reconstructing non-transmitted points, allowing for data reduction without losing relevant information, using criteria like radial distance and echo intensity to prioritize data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data reduction is performed by limiting the maximum data rate through distance or pulse energy criteria, then bandwidth requirements are reduced, but information loss increases causing relevant objects to be missed
Solution Approach 1:
The patent creates virtual copies of transmitted points through reconstruction algorithms. Instead of transmitting all actual points, it transmits a reduced set and generates synthetic copies of the remaining points using interpolation and geometric relationships, thereby reducing data volume while preserving complete information content.
Solution Approach 2:
The patent changes the representation parameters of point cloud data by transforming spatial coordinates into a compressed parameter space. It uses feature extraction and dimensionality reduction techniques to represent points with fewer parameters, achieving data reduction while maintaining the ability to reconstruct original information.
2Productivity
If only two or three points per pixel are transmitted based on distance or pulse energy criteria, then data transmission bandwidth is reduced, but detection reliability deteriorates due to missing reflections from small or poorly reflecting objects
Solution Approach 1:
The patent implements a feedback mechanism where the transmission strategy is dynamically adjusted based on scene complexity and object importance. The system evaluates detection confidence and adapts the reduction ratio accordingly, maintaining high reliability in critical scenarios while achieving aggressive compression in less critical areas.
Solution Approach 2:
The patent applies different reduction strategies to different regions of the point cloud based on local characteristics. High-priority regions containing small or poorly reflecting objects are preserved with minimal reduction, while low-priority regions undergo more aggressive compression, thereby maintaining detection reliability where it matters most.
3Loss of information
If comprehensive data transmission is performed without reduction, then information completeness is maintained, but bandwidth requirements exceed available capacity in modern LiDAR systems
Solution Approach 1:
The patent segments the point cloud data into multiple priority levels and categories based on object importance, spatial location, and reflection characteristics. This segmentation enables selective transmission where only essential segments are fully transmitted while others are reconstructed, achieving both information completeness and bandwidth efficiency.
Solution Approach 2:
The patent performs preliminary analysis and classification of point cloud data before transmission, identifying which points contain critical information and which can be reconstructed. This preliminary action enables optimized transmission strategies that preserve information completeness while minimizing data volume.
Data Source
AI summary
A method for reducing data with reduced information loss in LiDAR system including a transmitting unit and a receiving unit. In the method, in step a), all points of a point cloud to be compressed are compared for similarity to other points of the relevant point cloud. In step b), if a threshold value with regard to the similarity of a point to a further point is exceeded, the further point is not transmitted. For the not-transmitted point from the point cloud, additional information is generated in step c), which additional information allows reconstruction of the relevant not-transmitted point as a reconstructed point. In the case of sufficient reduction of data of the point cloud, the points are transmitted via a bandwidth-limited channel in step d). As soon as sufficient data reduction of the point cloud has been achieved, the data reduction is ended in step e).