ToF Sensor Super Voxel Grouping for Point Cloud Processing
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Solution Overview
Problem
Processing and segmentation of point clouds from time-of-flight sensors in autonomous vehicles require significant computing resources and time, often encountering interference from 'ghost point' clouds due to bad data and mis-mapping.
Innovation Solution
The system processes sensor data from Time-of-Flight cameras to identify super pixels and super voxels using machine learning models, where two-dimensional data is used to index pixels within 3D image data, facilitating the identification of scene elements and improving data processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If point cloud processing and segmentation are performed using traditional methods, then complete scene element identification is achieved, but computing resources and processing time are excessively consumed
Solution Approach 1:
The patent segments the point cloud data processing into multiple stages: initial filtering to remove obvious noise, superpixel formation to group related points, and refined segmentation to identify scene elements. This multi-stage segmentation approach reduces computational complexity at each step while maintaining identification accuracy.
Solution Approach 2:
The patent merges multiple processing operations into a unified pipeline that simultaneously performs noise filtering, superpixel formation, and scene element identification. By combining these operations and sharing computational resources across them, the system reduces overall processing time and resource consumption.
2Reliability
If traditional point cloud processing methods are used, then all data is processed in detail, but 'ghost point' cloud interference occurs due to bad data and mis-mapping
Solution Approach 1:
The patent extracts and removes ghost points and bad data early in the processing pipeline through statistical analysis and consistency checks. By identifying and extracting these harmful elements before they propagate through the processing system, the reliability of subsequent segmentation and identification operations is improved.
Solution Approach 2:
The patent converts the presence of ghost points and noise into useful information by analyzing their distribution patterns and characteristics. This analysis helps refine the segmentation parameters and improve the robustness of the identification algorithm, turning a harmful factor into a means for system improvement.
3Measurement precision
If detailed processing of all pixels and voxels is performed, then accurate scene element identification is achieved, but computational requirements increase significantly
Solution Approach 1:
The patent segments the detailed processing workload by first forming superpixels that group spatially related pixels, then processing only the representative points of each superpixel. This hierarchical segmentation reduces the number of individual voxel processing operations from millions to thousands, dramatically reducing computational energy consumption while preserving identification accuracy.
4Productivity
If traditional segmentation methods are used without superpixel grouping, then individual pixel processing is performed, but processing efficiency is reduced
Solution Approach 1:
The patent merges spatially adjacent pixels with similar characteristics into superpixels, preserving their spatial relationships and contextual information. This merging allows the system to process superpixels as unified entities rather than individual pixels, improving processing efficiency while maintaining the spatial relationship information needed for accurate scene element identification.
Data Source
AI summary
Systems and techniques are provided for processing image data from a time-of-flight sensor. An example method includes receiving a depth map and a two-dimensional image that each correspond to an image frame captured by a time-of-flight (ToF) sensor; identifying a plurality of pixels within the two-dimensional image that correspond to at least one scene element; identifying, based on the plurality of pixels, a plurality of voxels within the depth map that correspond to the at least one scene element; and grouping the plurality of voxels into a super voxel that corresponds to the at least one scene element.