LiDAR Data Compression Using Distance-Intensity Prediction
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Solution Overview
Problem
The increasing demand for high-resolution point cloud data from lidar in autonomous driving systems leads to significant bandwidth and storage pressures due to the large amount of data generated, necessitating an efficient compression algorithm for lidar data transmission and storage.
Innovation Solution
A method for compressing lidar data using run-length encoding and residual-based entropy encoding, taking advantage of the association between distance measurement data and signal intensity measurement data, where 0-value data is efficiently represented, and non-0 data is encoded based on residuals, reducing the bitstream occupancy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution point cloud data is transmitted and stored, then measurement precision is improved, but data transmission bandwidth and storage capacity are overwhelmed
Solution Approach 1:
The patent extracts and separates distance measurement data from signal intensity measurement data, applying different compression strategies to each. Distance measurement data is compressed using run-length encoding to extract and compress consecutive identical values, while signal intensity data is compressed using residual-based entropy encoding. This extraction principle allows the system to handle each data type optimally, reducing overall data amount while preserving measurement precision.
Solution Approach 2:
The patent changes the representation parameters of lidar data by encoding distance measurements using run-length encoding (changing from individual value storage to run-count pairs) and signal intensity using residual-based entropy encoding (changing from direct storage to predicted-residual storage). These parameter changes dramatically reduce the bitstream occupancy while maintaining the ability to reconstruct high-resolution point cloud data.
2Quantity of substance
If compression algorithms are applied to reduce data amount, then data transmission and storage efficiency is improved, but compression complexity increases
Solution Approach 1:
The patent segments the compression process into distinct stages: run-length encoding for distance measurement data, residual calculation for signal intensity data, and entropy encoding. Each segment handles a specific aspect of the compression task, making the overall complex process more manageable and implementable. The segmentation allows parallel processing and modular implementation, reducing the practical complexity burden.
Solution Approach 2:
The patent performs preliminary actions by pre-calculating residuals between signal intensity values and their predictions before entropy encoding. This preliminary residual calculation simplifies the subsequent encoding process and enables more efficient compression. The preliminary action of establishing prediction models before compression reduces the complexity of the main compression operation.
3Measurement precision
If more lidar beams are used to achieve high-resolution point cloud, then measurement precision is improved, but data transmission bandwidth requirement increases
Solution Approach 1:
The patent fundamentally changes the data representation parameters through run-length encoding and residual-based entropy encoding, reducing the bits required per lidar measurement from potentially 32-64 bits to significantly fewer bits. This parameter change enables high-resolution point cloud from multiple lidar beams to be transmitted at feasible bandwidths, as the compressed bitstream occupancy is dramatically reduced while preserving measurement precision.
Data Source
AI summary
Method and apparatus for compressing lidar data provide for obtaining to-be-compressed data, where the to-be-compressed data is measurement data collected by lidar through N transmissions, where N≥1. When the to-be-compressed data includes corresponding distance measurement data and signal intensity measurement data, the distance measurement data is compressed to obtain a first bitstream. Signal intensity measurement data is predicted based on the distance measurement data to obtain a second bitstream.