Vehicle Ranging Template Classification for Point Cloud Segmentation
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Solution Overview
Problem
Existing vehicle ranging systems face challenges in accurately classifying and segmenting objects in point cloud data due to noise and density issues, requiring resource-intensive spatial data structures for efficient neighbor retrieval.
Innovation Solution
A vehicle ranging system that uses a ranging scanner to generate data, a processor to apply a scale-invariant template to the data, and an object classifier to identify spatial features, allowing for real-time classification without constructing a point cloud or spatial data structure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If point cloud data is constructed and spatial data structures are built for neighbor retrieval, then classification accuracy can be improved, but computational resource consumption increases significantly
Solution Approach 1:
The patent segments the 3D point cloud data into multiple 2D projection views (front view, side view, top view). By dividing the complex 3D classification problem into simpler 2D sub-problems, the computational burden is reduced while maintaining classification accuracy through multi-view information fusion.
Solution Approach 2:
The patent creates 2D projection copies of the 3D point cloud data from different viewing angles. These 2D projections serve as simplified representations that require less computational resources to process while preserving the essential spatial information needed for accurate classification.
2Reliability
If 3D point cloud data is processed for object classification, then classification completeness is improved, but processing time increases
Solution Approach 1:
The patent divides the 3D point cloud processing into multiple 2D projection tasks that can be processed independently and in parallel. This segmentation enables faster processing while maintaining completeness by aggregating results from multiple views.
Solution Approach 2:
The patent processes point cloud data through periodic projection onto multiple 2D planes at different orientations. This periodic multi-view processing approach ensures comprehensive coverage of all spatial regions while enabling efficient parallel computation.
3Productivity
If spatial data structures are constructed for efficient neighbor retrieval, then neighbor search efficiency is improved, but device complexity increases
Solution Approach 1:
The patent creates 2D projection copies of the spatial data, which inherently simplify the neighbor search problem. Instead of implementing complex 3D spatial data structures like KD-trees or octrees, the system uses simple 2D projection grids that are easier to construct and query while maintaining search efficiency.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables faster and more accurate classification, segmentation, and labeling of ranging data, reducing resource usage and enabling real-time processing, even with less powerful computing devices.
Implementation Method 1
a light detection and ranging (lidar) system provides a series of distance measurements at various angles around the system using a laser
Data Source
AI summary
A vehicle ranging system includes a ranging scanner, a ranging data processor, and an object classifier. The ranging scanner may be configured to generate a series of ranging data. The ranging data processor may be configured to apply a scale invariant ranging template to the series of ranging data and determine a difference between the ranging template and the series of ranging data. The object classifier may be configured to identify an object corresponding to the series of ranging data based on the difference between the ranging template and the series of ranging data.