Adaptive Raster Resolution for Point-Cloud Sensor Data
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Solution Overview
Problem
Existing methods for processing sensor data from point clouds, such as those from radar and lidar sensors, face challenges in memory and computing power requirements due to uneven point densities, leading to inefficiencies in object recognition tasks.
Innovation Solution
The method involves projecting point clouds into rasters with varying cell sizes and densities, compressing high-density regions more extensively than low-density regions, and aligning attributes across different rasters to maintain spatial relationships, reducing memory and computing needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a uniform high-resolution raster is used for the entire point cloud, then recognition accuracy is improved, but memory and computing power requirements increase significantly
Solution Approach 1:
The patent applies different raster resolutions to different spatial regions based on point density. High-density regions use high-resolution rasters to maintain recognition accuracy, while low-density regions use low-resolution rasters to reduce memory and computing requirements. This local adaptation resolves the contradiction by optimizing resource allocation according to actual information content in each region.
Solution Approach 2:
The patent divides the point cloud into multiple regions with different point densities and processes each region separately with appropriate raster resolution. This segmentation allows the system to avoid applying high-resolution processing uniformly across the entire point cloud, thereby reducing overall computational burden while maintaining accuracy where needed.
2Quantity of substance
If the entire point cloud is compressed through multiple stages, then memory requirements decrease, but information loss increases in low-density regions
Solution Approach 1:
The patent applies different compression stage configurations to different regions. High-density regions undergo multiple compression stages to achieve significant memory reduction, while low-density regions undergo fewer compression stages to preserve their limited information content. This local differentiation resolves the contradiction between memory efficiency and information preservation.
3Productivity
If uniform compression stages are applied to all regions, then processing efficiency is improved, but information loss varies unevenly across regions
Solution Approach 1:
The patent configures the number of compression stages based on local point density characteristics. Regions with high point density are assigned more compression stages to maximize processing efficiency and memory reduction, while regions with low point density are assigned fewer stages to minimize information loss. This local optimization resolves the contradiction between overall processing efficiency and regional information preservation.
Data Source
AI summary
A method for processing sensor data, wherein the sensor data are present as a point cloud of individual points. The points are projected into at least two rasters with different resolutions. Points from a subregion of the point cloud are projected into a first raster with higher resolution and further points of the point cloud are projected into a second raster with lower resolution. Attributes from the first raster are compressed and arranged in the second raster before the attributes from the second raster are compressed.