Motion-Aware Keypoint Selection for ICP Accuracy
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Solution Overview
Problem
Conventional keypoint detectors for iterative closest point (ICP) algorithms suffer from high computation complexity and poor performance due to non-ideal feature pairs, which affects the robustness and accuracy of applications like SLAM and visual tracking.
Innovation Solution
A motion-aware keypoint selection system is introduced, comprising a pruning unit, a point quality estimation unit, and a suppression unit, which selects a region of interest (ROI) based on near edge regions, estimates point quality considering motion and depth, and applies non-maximal suppression to generate keypoints, reducing computation complexity while maintaining accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional keypoint detectors use all points for ICP algorithm, then the keypoint detection covers the entire image, but the computation complexity becomes high
Solution Approach 1:
The patent segments the image into multiple regions and selectively processes only the near edge regions (NER) instead of the entire image. The pruning unit identifies NER based on depth information, and the point quality estimation unit processes only points within these segmented regions, thereby reducing computation complexity while maintaining detection accuracy in critical areas.
Solution Approach 2:
The patent applies local quality by differentiating between near edge regions and other regions. Points in the NER are processed with higher priority and quality estimation, while points in other regions are either skipped or processed with reduced complexity. This localized approach maintains high measurement precision where needed without unnecessarily processing the entire image.
2Reliability
If conventional keypoint detectors process all points, then comprehensive coverage is achieved, but performance is degraded due to non-ideal feature pairs
Solution Approach 1:
The patent performs preliminary action by pre-identifying near edge regions using depth information before the main ICP processing. The pruning unit selectively marks NER based on depth thresholds, and the point quality estimation unit pre-evaluates points within these regions. This preliminary segmentation filters out non-ideal feature pairs before they enter the main processing pipeline, improving robustness without significantly reducing efficiency.
Solution Approach 2:
The patent extracts and removes non-ideal feature pairs by excluding points outside the near edge regions from comprehensive processing. The suppression unit removes keypoints that fail quality thresholds, and the system focuses computational resources only on points within NER that are more likely to be ideal feature pairs, thereby improving reliability while maintaining productivity.
3Measurement precision
If the system processes all image points for ICP, then complete feature matching is achieved, but the time required for computation increases
Solution Approach 1:
The patent segments the computational task by first dividing the image into near edge regions and other regions based on depth information. Only points within the NER undergo full processing through the point quality estimation unit, while points in other regions are skipped or processed minimally. This segmentation dramatically reduces the number of points requiring full processing, thereby reducing computation time while maintaining accuracy in critical regions.
Solution Approach 2:
The patent applies partial action by processing only the necessary subset of points (those in near edge regions) rather than all points in the image. The pruning unit identifies NER as the critical region, and the system performs full quality estimation and processing only for points within this partial region, achieving sufficient accuracy without the time cost of processing the entire image.
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 receives the ROI and generates point quality; and a suppression unit that receives the point quality and generates keypoints. In one embodiment, a near edge region (NER) is selected as the ROI. In another embodiment, the point quality estimation unit generates point quality according to point motion and point depth.