Motion-Aware Keypoint Selection for ICP Complexity Reduction
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Solution Overview
Problem
Conventional keypoint detectors in iterative closest point (ICP) algorithms suffer from high computation complexity and poor performance due to non-ideal feature pairs, affecting robustness and accuracy in applications like SLAM and visual tracking.
Innovation Solution
A motion-aware keypoint selection system is introduced, comprising a pruning unit to select regions of interest, a point quality estimation unit to assess point quality based on frame rate, and a suppression unit using non-maximal suppression to generate keypoints, reducing computation complexity while maintaining accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional keypoint detectors use all points for ICP algorithm, then the ICP algorithm can process complete point clouds, but the computation complexity increases significantly
Solution Approach 1:
The patent segments the point cloud into multiple regions of interest (ROIs) based on spatial distribution and feature density. Instead of processing all points uniformly, the system divides the point cloud into distinct regions and selectively processes only the relevant regions, reducing computation complexity while maintaining ICP algorithm effectiveness.
Solution Approach 2:
The patent applies local quality assessment to evaluate and select keypoints based on their specific properties such as feature density, motion consistency, and spatial distribution. Each region is assessed individually to determine its suitability for ICP processing, allowing the system to focus computational resources on high-quality regions while excluding low-quality areas.
2Loss of information
If conventional keypoint detectors use all points for ICP algorithm, then complete point cloud information is preserved, but the accuracy deteriorates due to non-ideal feature pairs
Solution Approach 1:
The patent performs preliminary keypoint selection and quality assessment before executing the ICP algorithm. By pre-filtering and ranking keypoints based on their quality metrics and spatial characteristics, the system prepares an optimized subset of keypoints that are most suitable for ICP matching, thereby improving accuracy while reducing the impact of non-ideal feature pairs.
Solution Approach 2:
The system incorporates feedback mechanisms to continuously evaluate the quality of selected keypoints and adjust the selection criteria dynamically. The feedback loop allows the system to refine its keypoint selection based on the actual performance of ICP matching, ensuring that only high-quality features are used while maintaining information completeness.
3Quantity of substance
If conventional keypoint detectors process all points, then comprehensive feature extraction is achieved, but the processing time increases
Solution Approach 1:
The patent applies partial action by selecting and processing only the necessary subset of keypoints rather than all available points. The system determines the optimal number and location of keypoints to process based on the point cloud characteristics and ICP requirements, achieving sufficient feature extraction without the time cost of processing every point in the cloud.
Data Source
AI summary
A motion-aware keypoint selection system adaptable to iterative closest point (ICP) includes a pruning unit that receives an image and selects at least one region of interest (ROI) composed of a selected subset of points on the image; a point quality estimation unit that generates point quality of each point in the ROI according to a frame rate; and a suppression unit that receives the point quality and generates keypoints by screening the ROI.