LiDAR Data Compression for Rapid Attribute-Based Retrieval
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Solution Overview
Problem
LiDAR output data files in the LAS format can grow to multiple terabytes in size due to inefficient storage of point attributes, leading to significant storage waste and the need to read and decompress entire datasets for filtering, which is inefficient and resource-intensive.
Innovation Solution
A point data processing system that uses Run Length Encoding (RLE) and other compression techniques to reduce storage requirements and enable rapid access to filtered data by converting point data into a compressed format, allowing only necessary fields and data points to be retrieved without decompressing the entire dataset, utilizing a 'point bag' data structure and indexing for efficient retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If LiDAR data is stored in the standard LAS file format with all point attributes, then complete data information is preserved, but file size grows to multiple terabytes causing significant storage waste
Solution Approach 1:
The patent extracts and stores only the essential attributes of LiDAR points (such as position, intensity, and classification) while omitting redundant or less critical attributes. This selective extraction reduces storage requirements while preserving the most important data information for typical applications.
Solution Approach 2:
Instead of storing complete point attributes and filtering later, the patent inverts the approach by pre-processing and compressing the data structure to store only necessary information. This inversion occurs during data acquisition or initial processing rather than during retrieval operations.
2Ease of operation
If the entire LiDAR dataset is read and decompressed for filtering operations, then complete data access is achieved, but processing time and resource consumption increase significantly
Solution Approach 1:
The patent performs preliminary actions during data storage by organizing and compressing the LiDAR data into an efficient structure with pre-computed indices. This preliminary organization enables rapid querying and filtering operations without requiring full data decompression, as the compressed structure allows direct access to relevant portions.
Solution Approach 2:
The patent introduces an intermediate compressed data structure that serves as a mediator between the original LiDAR data and the filtering operations. This intermediate structure allows efficient querying and partial decompression, acting as a buffer that reduces the computational burden of full data processing while maintaining data accessibility.
3Device complexity
If all point attributes are stored with fixed allocation, then data structure simplicity is maintained, but storage efficiency decreases due to wasted space on null values
Solution Approach 1:
The patent applies local quality by allowing different parts of the data structure to have different properties. Instead of uniform fixed allocation for all points, the structure adapts locally to store only the attributes that are actually present and relevant for each point or group of points, reducing wasted space on null values while maintaining simplicity where applicable.
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 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.