3D Scan Point Clustering for Ghost Pixel Removal
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Solution Overview
Problem
Existing recognition technologies face challenges in accurately identifying moving objects between stationary objects and overlapping objects in a scan space due to the formation of erroneous 'ghost pixels' from overlapping scan echoes, leading to reduced recognition accuracy.
Innovation Solution
A processor-executed recognition system that acquires three-dimensional scan data, reads a three-dimensional dynamic map, and clusters scan points based on identification information for each voxel in the scan space, allowing for the exclusion of erroneous scan points and accurate recognition of moving objects between or overlapping with stationary objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If scanning is performed in a scan space containing stationary objects, then the scan space can be mapped, but erroneous ghost pixels are formed from overlapping scan echoes reducing recognition accuracy
Solution Approach 1:
The scan space is divided into multiple three-dimensional voxels, and identification information is assigned to each voxel to distinguish between stationary objects and moving objects. This segmentation allows the system to process scan points based on their spatial location and characteristics, enabling accurate identification of moving objects even in the presence of ghost pixels from stationary objects.
Solution Approach 2:
The system extracts and removes erroneous scan points (ghost pixels) from the scan data by comparing scan points against the three-dimensional dynamic map and using identification information from voxels. This extraction process eliminates harmful ghost pixels while preserving valid scan points for accurate moving object recognition.
2Measurement precision
If clustering is performed on all scan points, then object detection can be conducted, but recognition accuracy deteriorates due to inclusion of erroneous ghost pixels
Solution Approach 1:
The system extracts only the necessary valid scan points for clustering by removing erroneous ghost pixels through comparison with the three-dimensional dynamic map and voxel identification information. This selective extraction ensures that clustering is performed only on meaningful scan points, maintaining recognition accuracy while reducing unnecessary processing.
Solution Approach 2:
Different processing approaches are applied to different regions of the scan space based on voxel identification information. Valid scan points in regions containing moving objects are processed with clustering, while erroneous ghost pixels in regions with stationary objects are excluded. This local quality approach optimizes recognition accuracy by applying appropriate processing to each region.
3Measurement precision
If the scan space is divided into multiple three-dimensional voxels with identification information, then moving objects can be accurately recognized, but the system complexity increases
Solution Approach 1:
The scan space is segmented into three-dimensional voxels with identification information to enable accurate moving object recognition. This segmentation provides a structured framework for organizing scan data and applying appropriate processing to each voxel, improving recognition accuracy while maintaining manageable system complexity through systematic data organization.
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
The system effectively excludes erroneous scan points, enabling high-accuracy recognition of moving objects between or overlapping with stationary objects by adjusting clustering ranges based on identification information, thereby improving recognition reliability.
Implementation Method 1
scanning the target moving object using a scanning device mounted on a host moving object; acquire three-dimensional scan data representing a scan point group generated by scanning of the scan space
Data Source
AI summary
The present disclosure provides a recognition technology to be executed by a processor. The processor, by executing a program stored in a computer-readable non-transitory storage, is configured to recognize, in a scan space, a target moving object that is movable in the scan space by scanning the target moving object using a scanning device mounted on a host moving object; acquire three-dimensional scan data representing a scan point group generated by scanning of the scan space; read, from a storage medium, a three-dimensional dynamic map representing a mapping point group generated by mapping of an object present in the scan space; and cluster the scan point group based on identification information for identifying a state of the scan space in each of multiple three-dimensional voxels into which the scan space is divided in the three-dimensional dynamic map and generate recognition data by recognizing the target moving object.