LiDAR Point Cloud Streaming with Common-Format Visualization
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Solution Overview
Problem
Existing point cloud processing systems are limited in handling continuous streaming of LiDAR data and face challenges in visualizing data from multiple LiDAR sources due to proprietary format discrepancies and the need to analyze static-scene point cloud data.
Innovation Solution
A system that constructs point cloud and image data structures with header blocks and blocks to stream LiDAR data, allowing for continuous processing and visualization by assigning LiDAR data into point cloud data structures and image data structures, facilitating the translation into a common format for analysis and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If proprietary LiDAR data formats are used directly, then data can be stored in native format, but it becomes difficult to stream continuously and visualize data from multiple LiDAR sources due to format discrepancies
Solution Approach 1:
The patent introduces a standardized intermediate data structure that acts as a mediator between proprietary LiDAR formats and processing systems. This intermediate format includes standardized fields for point cloud data, headers, and metadata that can accommodate data from multiple LiDAR sources while maintaining a consistent structure for continuous streaming and processing.
Solution Approach 2:
The patent transforms proprietary LiDAR data formats by changing their structural parameters to match a standardized format. This involves converting data organization, coordinate systems, and data representation methods while preserving the essential LiDAR information, enabling compatibility across different sources and continuous processing capabilities.
2Productivity
If static-scene point cloud data is processed, then analysis can be performed on individual scenes, but continuous streaming of dynamic LiDAR data sequences cannot be visualized
Solution Approach 1:
The patent creates a dynamic data structure that can handle continuously arriving LiDAR data frames in real-time. The structure supports incremental data addition, time-stamping, and sequential processing of dynamic scenes, allowing the system to adapt to changing data streams while maintaining processing efficiency through optimized memory management and batch processing capabilities.
3Measurement precision
If proprietary visualization software is developed for each LiDAR model, then accurate visualization can be achieved, but system complexity increases and cross-platform compatibility decreases
Solution Approach 1:
The patent designs a universal data structure and processing framework that can handle data from multiple LiDAR sources and models through a single standardized interface. The structure includes flexible fields that can accommodate different LiDAR specifications while maintaining a common processing pipeline, eliminating the need for separate specialized software for each LiDAR model.
Data Source
AI summary
A system includes a computer-readable memory for storage and retrieval of LiDAR data. The memory includes one or more point cloud data structures, each point cloud data structure including a first header block, and a plurality of point blocks. Each of the plurality of point blocks is configured to store at least a portion of LiDAR point cloud data. The memory includes one or more image data structures having a second header block, and a plurality of image column blocks. Each image column block includes a column of pixels of a corresponding image from the LiDAR point cloud data, and the image column blocks collectively contain all pixels of the corresponding image.