LiDAR Data Compression for Fast Attribute-Based Retrieval
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
LiDAR output data storage requires significant disc space and current methods are inefficient, leading to large file sizes and storage challenges, especially in LAS file format, where unused data fields occupy space and require decompression for attribute filtering.
Innovation Solution
A point data processing system employing Run Length Encoding, delta encoding, and data smoothing to compress LiDAR data, allowing for rapid access to filtered data without decompressing the entire dataset, by converting data into a column-first format and using specialized indexes for efficient retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If LiDAR data is stored in LAS file format with all point attributes, then data completeness and readability are improved, but file size and storage requirements increase significantly
Solution Approach 1:
The patent extracts and removes unused or redundant data fields from each LiDAR point record. By identifying and eliminating fields that are not needed for specific applications (such as removing certain attribute fields when they are not required), the system significantly reduces file size while preserving only the essential data needed for the intended use case.
Solution Approach 2:
The patent applies different data storage strategies to different fields based on their specific characteristics and requirements. Rather than uniformly storing all fields with the same precision and format, the system optimizes each field individually - using appropriate data types, precision levels, and compression methods tailored to each attribute's needs, thereby reducing overall storage requirements while maintaining necessary data quality.
2Quantity of substance
If LiDAR data is compressed using traditional methods, then storage space is reduced, but retrieval speed decreases due to decompression requirements
Solution Approach 1:
The patent performs data filtering and organization operations during the data collection and initial processing phase, rather than during retrieval. By pre-filtering the data according to specified criteria (such as point classification, intensity ranges, or spatial boundaries) and organizing it into efficiently accessible structures, the system enables rapid retrieval without requiring full decompression, thus maintaining both storage efficiency and retrieval speed.
3Measurement precision
If all LiDAR data fields are stored with full precision, then measurement accuracy is maintained, but storage requirements and processing overhead increase
Solution Approach 1:
The patent applies different precision levels to different data fields based on their specific requirements. For fields where high precision is critical (such as spatial coordinates), the system maintains full precision storage. For fields where extreme precision is not necessary (such as certain attribute classifications or derived parameters), the system uses optimized data types with appropriate precision levels, thereby reducing overall storage requirements while maintaining measurement accuracy where it matters most.
Data Source
AI summary
The present invention relates to a method and system for compressing and retrieving Light Detection and Ranging output data, and, more specifically, to a method and system for compressing Light Detection and Ranging output data by Run Length Encoding or losslessly compressing Light Detection and Ranging output data and rapidly accessing this compressed data which is filtered by attributes without the need to read or decompress the entire collection of data.