LiDAR Point Cloud Compression via Differential Coordinates
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Solution Overview
Problem
LiDAR systems generate large amounts of point cloud data due to increased resolution, leading to bandwidth limitations and inefficient data streaming, with existing compression methods not effectively identifying regions with dense point clusters for compression.
Innovation Solution
A computer-implemented method for compressing LiDAR point cloud data by identifying sub-groups of dense point clusters within the field-of-view and encoding them using differential coordinates, reducing the number of bits required for encoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LiDAR resolution is increased to improve three-dimensional perception quality, then measurement precision is improved, but the quantity of point cloud data increases causing bandwidth limitations
Solution Approach 1:
The patent divides the point cloud data into multiple subsets based on spatial regions (e.g., above-horizon and below-horizon regions). This segmentation allows different compression strategies to be applied to different regions, reducing the overall data volume while preserving important perceptual information in critical regions.
Solution Approach 2:
The patent applies differential compression techniques where regions with higher perceptual importance (such as above-horizon regions containing important objects) are compressed with higher fidelity, while less critical regions (such as below-horizon regions) are compressed with lower fidelity. This local quality approach maintains measurement precision for important areas while reducing total data volume.
2Measurement precision
If all point cloud data is transmitted with high fidelity to maintain perception accuracy, then measurement precision is improved, but data transmission bandwidth requirements increase
Solution Approach 1:
The patent changes the coordinate system parameters from absolute coordinates to differential coordinates (relative to reference points). This parameter transformation reduces the number of bits required to represent each point while maintaining the ability to reconstruct accurate three-dimensional perception, thereby reducing bandwidth consumption without sacrificing perception accuracy.
3Quantity of substance
If compression is applied to reduce data volume, then quantity of substance is reduced, but loss of information may occur affecting perception quality
Solution Approach 1:
The patent performs preliminary organization of point cloud data into structured subsets with defined reference points before compression. By pre-establishing the differential coordinate relationships and identifying key reference points, the compression process can efficiently reduce data volume while preserving the essential geometric relationships needed for accurate perception reconstruction.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables efficient data compression, allowing for better data streaming with limited bandwidth while maintaining the ability to construct a three-dimensional perception of the environment.
Implementation Method 1
At least one of the 3-dimensional coordinates is derived from a ToF measured by transmitting a light beam to the FOV and receiving return light formed based on the transmitted light beam
Data Source
AI summary
A computer-implemented method for compressing point cloud data obtained by a LiDAR system is provided. The method comprises obtaining uncompressed point cloud data. Each of the uncompressed point cloud data is represented by 3-dimensional coordinates identifying positions within a field-of-view of the LiDAR system. At least one of the 3-dimensional coordinates is derived from a ToF measured by transmitting a light beam and receiving return light formed based on the transmitted light beam. The method further comprises identifying one or more sub-groups of the uncompressed point cloud data for compression. The method further comprises encoding the one or more sub-groups of the uncompressed point cloud data using differential coordinates to obtain first encoded point cloud data, and providing the first encoded point cloud data to a processor to construct at least a part of a three-dimensional perception of the FOV.