RADAR Point Cloud Compression for Autonomous Map Localization
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Solution Overview
Problem
Current systems for generating maps and performing localization in autonomous vehicles and machines using sensor data, such as RADAR and LIDAR, face challenges in efficiently processing and communicating large data sets while maintaining accuracy and precision.
Innovation Solution
The system compresses RADAR point clouds using techniques like quantization and tile-based encoding, and performs localization by determining pose parameters through comparison with reference data, reducing data transmission while maintaining map generation and localization accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If RADAR point clouds are transmitted without compression, then map generation accuracy is maintained, but data transmission volume increases
Solution Approach 1:
The patent applies quantization to transform continuous RADAR point cloud data into discrete representations, changing the data parameters from high-precision floating-point values to compressed integer codes. This parameter transformation reduces data transmission volume while preserving sufficient accuracy for map generation and localization tasks.
Solution Approach 2:
The system extracts and transmits only the essential features and characteristics of RADAR point clouds needed for map generation, rather than transmitting the complete raw data set. This selective extraction reduces transmission volume while maintaining the accuracy required for autonomous vehicle operations.
2Quantity of substance
If data compression is applied to RADAR point clouds, then data transmission volume is reduced, but processing complexity increases
Solution Approach 1:
The patent performs compression operations on RADAR point clouds at the source vehicle before transmission, rather than at the receiving end. This preliminary compression reduces the burden on the central server's processing resources while maintaining data quality sufficient for accurate map generation and localization.
Solution Approach 2:
The system creates compressed representations (copies) of the original RADAR point cloud data that retain the essential information needed for map generation. These compressed copies are transmitted instead of the full-resolution data, reducing transmission volume and processing requirements while maintaining functional accuracy.
3Quantity of substance
If quantization is used to compress point cloud data, then data volume is reduced, but localization precision may be compromised
Solution Approach 1:
The patent applies different quantization levels to different regions of the point cloud data based on their importance for localization. Critical regions near the vehicle maintain higher precision, while distant regions use coarser quantization. This localized quality adjustment reduces overall data volume while preserving localization precision where it matters most.
Data Source
AI summary
One or more embodiments of the present disclosure relate to generating RADAR (RAdio Detection And Ranging) point clouds based on RADAR data obtained from one or more RADAR sensors disposed on one or more ego-machines. In these or other embodiments, the RADAR point clouds may be communicated to a distributed map system that is configured to generate map data based on the RADAR point clouds. In some embodiments of the present disclosure, certain compression operations may be performed on the RADAR point clouds to reduce the amount of data that is communicated from the ego-machines to the map system.