LiDAR Point Cloud Compression via Raster Formatting
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Solution Overview
Problem
Autonomous vehicle navigation systems face challenges in efficiently processing and storing the large amounts of data generated by LiDAR sensors, particularly due to high storage and bandwidth requirements, necessitating a lossless data compression method to maintain data integrity.
Innovation Solution
Formatting point cloud data from LiDAR sensors into a raster-graphic data frame and applying lossless compression algorithms, such as LZ77 and Huffman coding, to reduce file size by up to 80%.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If LiDAR point cloud data is stored and transmitted in uncompressed format, then data integrity and measurement precision are maintained, but storage requirements and bandwidth consumption increase significantly
Solution Approach 1:
The patent changes the parameter representation from uncompressed binary point cloud data to compressed format while maintaining data integrity through lossless compression algorithms. The system transforms the data representation to reduce storage requirements without sacrificing measurement precision or data accuracy.
Solution Approach 2:
The patent creates a compressed copy of the point cloud data that preserves the essential information. By applying lossless compression, the system generates a reduced-size representation that can be stored and transmitted efficiently while allowing exact reconstruction of the original data when needed.
2Quantity of substance
If LiDAR point cloud data is compressed to reduce storage and bandwidth needs, then storage requirements and transmission cost decrease, but data reconstruction accuracy may be compromised
Solution Approach 1:
The patent employs lossless compression algorithms that change the data parameters through reversible transformation. This ensures that the compressed data can be exactly reconstructed without any loss of precision or accuracy, thereby maintaining measurement fidelity while reducing storage requirements.
Solution Approach 2:
The system creates an accurate copy of the original point cloud data through lossless compression. The compressed representation preserves all essential information and geometric details, allowing for exact reconstruction of the original data structure, measurements, and spatial relationships.
3Productivity
If point cloud data is processed and compressed in real-time, then data management efficiency improves, but processing complexity and computational requirements increase
Solution Approach 1:
The patent applies compression algorithms to point cloud data in real-time as it is generated and transmitted. By performing compression preliminarily and continuously, the system improves data management efficiency without requiring complex post-processing steps, thereby reducing overall processing complexity.
Solution Approach 2:
The compression system is integrated into the data pipeline to automatically compress point cloud data as it flows through the system. This self-service approach eliminates the need for separate manual compression steps and reduces processing complexity by making compression an inherent part of data handling.
Data Source
AI summary
Aspects of the disclosed technology provide solutions for compressing a point cloud of LiDAR data collected for autonomous vehicle (AV) navigation. A process of the disclosed technology can include steps for receiving point cloud data from a LiDAR sensor coupled to an AV, formatting a portion of the received point cloud data according to a raster graphic-image file standard to create a data frame, and compressing the data frame using a lossless compression algorithm. Systems and machine-readable media are also provided.