LiDAR Point Cloud Compression via 2D Bitmap Mapping
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Point cloud data from LiDAR devices generates large file sizes when converted into three-dimensional orthogonal coordinates, making data compression challenging, especially for real-time transmission in autonomous driving applications where file size reduction is crucial for communication efficiency.
Innovation Solution
A data compression apparatus that converts LiDAR point cloud data into bitmap images using three-dimensional coordinate values as color signals, allowing for the application of existing image compression techniques like JPEG to effectively reduce file size while maintaining spatial continuity for compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If point cloud data is converted into three-dimensional orthogonal coordinate system, then the data can be processed and visualized more easily, but the file size increases significantly
Solution Approach 1:
The patent changes the parameter representation by converting 3D orthogonal coordinates into a 2D bitmap image format. This parameter transformation allows the use of image compression techniques on the coordinate data, effectively reducing file size while preserving the spatial information needed for processing and visualization.
Solution Approach 2:
The patent creates a bitmap image copy of the point cloud data where each pixel represents a 3D coordinate. This copying approach allows the original coordinate information to be preserved in a compressed visual format, enabling easy processing while reducing the amount of data that needs to be stored and transmitted.
2Measurement precision
If mesh granularity becomes finer to improve spatial resolution, then the three-dimensional space is expressed more accurately, but the file size increases explosively in proportion to the cube of the granularity
Solution Approach 1:
The patent resolves this contradiction by transitioning from a 3D mesh representation to a 2D bitmap representation. This dimensionality change allows fine spatial resolution to be maintained through image pixel density while avoiding the cubic growth of file size that occurs with 3D mesh refinement. The 2D image format provides a more efficient scaling characteristic for high-resolution spatial data.
3Productivity
If point cloud data is transmitted in real-time for autonomous driving applications, then the data utility is maximized, but the communication load and cost increase due to large data volume
Solution Approach 1:
The patent applies parameter changes by converting the data format to a compressed bitmap representation that maintains the essential spatial information while dramatically reducing the data volume. This enables real-time transmission over mobile networks for autonomous driving applications, as the compressed format significantly lowers communication load and cost while preserving the utility needed for navigation and obstacle detection.
Data Source
AI summary
The object of the present invention is to provide a technique capable of effectively compressing point cloud data output from the ranging sensor. A data compression apparatus of the present invention includes a data acquisition unit (121) which acquires point cloud data including a plurality of sets each including a direction and a distance from a ranging sensor (11), a data conversion unit (122) which converts directions and distances in the sets into color signals, an image generation unit (123) which generates a bitmap Image by mapping the color signals into a two-dimensional data structure in accordance with the directions, and an image compression unit (124) which applies a predetermined image compression technique to the bitmap image.