LIDAR Point Cloud Compression via 2D Image Mapping
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Solution Overview
Problem
Current technologies lack effective compression methods for Light Detection And Ranging (LIDAR) sensor data, leading to storage and transmission challenges in autonomous driving systems, with no widely accepted industry standards and inefficient use of bandwidth and storage resources.
Innovation Solution
The proposed solution involves organizing LIDAR sensor data into a 2-D image array and applying image compression techniques, such as JPEG-LS or PNG, after converting raw distance data to X, Y, Z point positions and re-ordering data points based on scanning order, to exploit 2-D correlation and achieve higher compression ratios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LIDAR sensor data is stored and transmitted without compression, then data integrity and measurement precision are maintained, but storage space and bandwidth consumption increase significantly
Solution Approach 1:
The patent transforms 3D point cloud data into a 2D image array representation by mapping spatial coordinates to image pixels. This dimensional transformation enables the application of 2D image compression algorithms to LIDAR data, achieving significant compression ratios while preserving essential spatial information. The point cloud data is organized into a grid structure where each pixel represents a vertical slice of the point cloud, allowing efficient compression without complete loss of spatial relationships.
Solution Approach 2:
The patent creates a simplified 2D copy of the 3D point cloud data in the form of an image array. This copy captures the essential spatial structure and intensity information of the original data in a more compact format. By working with this 2D representation rather than the full 3D point cloud, the system achieves compression while maintaining sufficient information for autonomous driving applications.
2Measurement precision
If LIDAR sensor data is stored and transmitted without compression, then data quality is preserved, but bandwidth consumption and transmission time increase
Solution Approach 1:
The transformation to 2D image array format enables efficient bandwidth utilization by allowing standard image compression techniques to be applied. This dimensional change reduces the data volume transmitted over the network while preserving the spatial structure necessary for quality reconstruction.
Solution Approach 2:
The patent changes the representation parameters of LIDAR data from 3D coordinates to 2D image pixel values with associated intensity information. This parameter transformation allows the use of optimized compression algorithms that operate on 2D data structures, reducing bandwidth consumption while maintaining data quality through controlled compression levels.
3Quantity of substance
If point cloud data is compressed using traditional methods, then some compression is achieved, but compression ratio and performance are insufficient compared to image-based approaches
Solution Approach 1:
By converting 3D point cloud data to a 2D image array representation, the patent enables the use of highly optimized 2D image compression algorithms. This dimensional transformation is key to achieving superior compression ratios, as it allows the exploitation of spatial correlations in the image domain that are not easily accessible in the original 3D point cloud format.
Solution Approach 2:
The patent applies universal image compression techniques (such as JPEG, PNG, or other lossless/lossy compression algorithms) to LIDAR point cloud data after transforming it into image array format. This multi-functional approach allows the use of well-established, highly efficient compression methods originally designed for images to be applied to spatial data, achieving high compression ratios while maintaining data quality.
Data Source
AI summary
Methods and apparatus relating to image-based compression of Light Detection And Ranging (LIDAR) sensor data with point re-ordering are described. In an embodiment, logic receives distance sensor data and converts the received distance sensor data to point cloud data. The point cloud data corresponds to a set of points in a three dimensional (3D) space. The logic circuitry packs/organizes the converted point cloud data into one or more two dimensional (2D) arrays. Data stored in the one or more 2D arrays are compressed to generate a compressed version of the point cloud data. Other embodiments are also disclosed and claimed.