3D Point Cloud Generation via Multi-Sensor Fusion
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Solution Overview
Problem
Conventional methods for generating three-dimensional point clouds are limited by low processing speed and quality due to the use of a single image data source, which restricts their application in fields like automatic driving and SLAM.
Innovation Solution
A method utilizing multiple sensors to obtain and fuse image data sources, including depth cameras, binocular cameras, and position angle sensors, to improve the efficiency and accuracy of generating three-dimensional point clouds through advanced fusion processing techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single image data source is used for three-dimensional point cloud generation, then the device complexity is reduced, but the processing speed and quality of the point cloud are limited
Solution Approach 1:
The patent combines multiple image data sources (depth camera, binocular camera, monocular camera, position angle sensor) into a unified fusion processing system. By merging these multiple data sources, the system achieves higher processing speed and quality point cloud generation while managing device complexity through integrated processing architecture.
Solution Approach 2:
The fusion processing unit serves multiple functions by accepting and processing various types of image data sources from different sensor types. This multi-functionality allows the system to leverage diverse data sources for enhanced point cloud generation without requiring separate dedicated processing pipelines for each sensor type.
2Manufacturing precision
If a single image data source is used for three-dimensional point cloud generation, then the device complexity is reduced, but the quality of the three-dimensional point cloud is limited
Solution Approach 1:
The patent merges multiple image data sources including depth cameras, binocular cameras, monocular cameras, and position angle sensors into a comprehensive fusion processing system. This combination enables higher quality three-dimensional point cloud generation by integrating information from multiple perspectives and sensor types, while managing device complexity through unified processing architecture.
Solution Approach 2:
The system creates a composite data fusion approach by combining different types of image data sources (depth, binocular, monocular, position angle) to generate high-quality point clouds. This composite material analogy refers to how diverse data sources are integrated to produce superior output quality that cannot be achieved by any single data source alone.
3Manufacturing precision
If multiple sensors and image data sources are used for fusion processing, then the speed and quality of three-dimensional point cloud generation are improved, but the device complexity increases
Solution Approach 1:
The patent merges multiple sensors and image data sources into a unified fusion processing system that handles depth cameras, binocular cameras, monocular cameras, and position angle sensors through a single processing pipeline. This merging approach improves quality while managing device complexity by consolidating processing functions rather than treating each sensor separately.
Data Source
AI summary
A method of generating a three-dimensional point cloud includes obtaining a plurality of image data sources through a plurality of sensors, and performing fusion processing according to the plurality of image data sources to obtain the three-dimensional point cloud.