Compact LiDAR Representation via Vector Quantization
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Solution Overview
Problem
LiDAR systems face challenges with sparse data capture, especially at distant objects and in poor weather conditions, and are expensive, limiting their accessibility and scalability for autonomous systems.
Innovation Solution
A machine learning framework that includes an encoder model to convert 3D LiDAR images into continuous embeddings, followed by vector quantization using a code map to generate discrete embeddings, and a decoder model to produce modified LiDAR data, enhancing data density and robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LiDAR systems use time-of-flight scanning with rotating emitter-detector pairs, then accurate geometric measurements are obtained, but point cloud density decreases as distance increases
Solution Approach 1:
The patent creates a digital twin (virtual copy) of the physical LiDAR system including the vehicle, sensor, and environment. This digital replica allows simulation of LiDAR point cloud generation without actual hardware, enabling dense data generation for distant objects through virtual scanning rather than relying solely on sparse physical measurements
Solution Approach 2:
The system pre-generates dense LiDAR point clouds through simulation before actual autonomous operation. By creating comprehensive virtual training data in advance covering diverse scenarios and distances, the system overcomes the sparsity problem of real LiDAR data without requiring extensive physical data collection
2Quantity of substance
If dense LiDAR systems are used to capture high-density point clouds, then data quality improves, but system cost increases significantly
Solution Approach 1:
The patent replaces expensive physical LiDAR hardware with a computational model (digital twin) that simulates LiDAR behavior. This virtual copy generates dense point clouds through software processing rather than requiring costly dense LiDAR sensors, making high-quality data generation accessible without hardware investment
Solution Approach 2:
The invention substitutes the mechanical LiDAR scanning system with a software-based simulation approach. Instead of using physical emitter-detector pairs that rotate and scan, the system uses computational algorithms to generate point clouds, replacing expensive hardware with more accessible software solutions
3Quantity of substance
If more LiDAR beams are used to improve data coverage, then point cloud density increases, but device complexity and cost increase
Solution Approach 1:
The digital twin system serves multiple functions: it simulates various LiDAR configurations, generates training data for different scenarios, and models different weather conditions all within a single software platform. This multi-functional approach replaces the need for multiple physical LiDAR systems with different beam configurations
Data Source
AI summary
Compact LiDAR representation includes performing operations that include generating a three-dimensional (3D) LiDAR image from LiDAR input data, encoding, by an encoder model, the 3D LiDAR image to a continuous embedding in continuous space, and performing, using a code map, a vector quantization of the continuous embedding to generate a discrete embedding. The operations further include decoding, by the decoder model, the discrete embedding to generate modified LiDAR data, and outputting the modified LiDAR data.