Adaptive Segmentation Parameters for 3D Spatial Imaging
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Solution Overview
Problem
Current three-dimensional (3D) spatial measurement technologies for machine vision in autonomous vehicles face challenges in efficiently detecting and recognizing objects due to uneven point cloud distributions and varying environmental conditions, such as distance and weather, which affect the accuracy and reliability of segmentation operations.
Innovation Solution
The proposed solution involves dynamically adjusting segmentation algorithm parameters based on point-cloud density and environmental conditions, using a combination of range-based and environment-based adjustment functions to optimize parameter values for different ranges and conditions, thereby improving the detection of objects in the field of view.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a fixed segmentation algorithm is used for all ranges, then the device complexity is reduced, but the measurement precision deteriorates due to uneven point cloud distributions at different distances
Solution Approach 1:
The patent applies dynamics by making the segmentation algorithm adaptive rather than fixed. The system dynamically adjusts segmentation parameters based on the range distance to objects, allowing the algorithm to respond to varying point cloud densities. This is achieved through range-based parameter adjustment where closer objects use different segmentation thresholds than distant objects, resolving the contradiction between maintaining high precision across all ranges and avoiding excessive system complexity.
Solution Approach 2:
The patent implements parameter changes by modifying segmentation algorithm parameters according to range distance. Different parameters are applied to different distance zones, allowing optimal segmentation for both near and far objects. This approach improves measurement precision by tailoring parameters to specific ranges while managing complexity through systematic parameter adjustment rather than completely different algorithms for each scenario.
2Reliability
If standard segmentation parameters are used for distant objects, then the ease of operation is maintained, but the reliability deteriorates due to sparse point cloud distributions
Solution Approach 1:
The patent applies local quality by implementing range-specific segmentation parameters tailored to local point cloud characteristics. Distant objects with sparse point distributions receive optimized parameters different from those used for close objects. This ensures reliable detection across varying distances while maintaining operational simplicity through automated range-based parameter selection, eliminating the need for manual parameter adjustment for different scenarios.
3Measurement precision
If manual parameter adjustment is performed for each scenario, then the measurement precision is improved, but the productivity decreases due to time-consuming calibration
Solution Approach 1:
The patent implements self-service by enabling the segmentation system to automatically adjust its own parameters based on detected range distances. The system performs self-calibration by identifying object distances and autonomously selecting appropriate segmentation parameters without external intervention. This maintains high measurement precision while significantly improving productivity by eliminating manual parameter adjustment time and enabling real-time adaptive segmentation.
Data Source
AI summary
Machine vision processing includes capturing 3D spatial data representing a field of view and including ranging measurements to various points within the field of view, applying a segmentation algorithm to the 3D spatial data to produce a segmentation assessment indicating a presence of individual objects within the field of view, wherein the segmentation algorithm is based on at least one adjustable parameter, and adjusting a value of the at least one adjustable parameter based on the ranging measurements. The segmentation assessment is based on application of the segmentation algorithm to the 3D spatial data, with different values of the at least one adjustable parameter value corresponding to different values of the ranging measurements of the various points within the field of view.