3D Point Connected Set Recognition for Robot Article Pickup
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Solution Overview
Problem
Existing article pickup systems require significant time and effort to prepare model patterns for each type of article, especially when dealing with a large number of different articles, and often necessitate user-teaching operations for accurate recognition and extraction.
Innovation Solution
An article pickup apparatus and method that utilizes a three-dimensional measurement instrument and camera to acquire position information and image data, determining connected sets of three-dimensional points to identify article positions and postures without the need for pre-prepared models or user teaching, using gradient information to judge point connections and calculate hand position postures for robotic pickup.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If model patterns are prepared for each type of article to enable accurate recognition, then recognition accuracy is improved, but time and effort for preparation increase significantly
Solution Approach 1:
The system performs self-service by automatically generating model patterns through image processing and feature extraction from captured images, eliminating the need for manual model preparation. The apparatus autonomously identifies article features and creates recognition models on-demand, allowing accurate recognition without time-consuming pre-preparation of models for each article type.
Solution Approach 2:
Instead of manually creating model patterns, the system creates digital copies of articles by processing images and extracting feature information. These copied feature representations serve as recognition models, enabling accurate identification without manual model preparation while significantly reducing preparation time and effort.
2Measurement precision
If teaching operations are performed for each article type to enable accurate extraction, then extraction accuracy is improved, but time and effort increase significantly
Solution Approach 1:
The system eliminates manual teaching operations by performing self-service through automatic feature extraction and learning. The apparatus autonomously processes images, extracts article features, and builds recognition capabilities without requiring user intervention or teaching time, achieving both high accuracy and operational efficiency.
Solution Approach 2:
Manual teaching operations are replaced with automated image processing and feature extraction mechanisms. The system uses computational algorithms to automatically learn article characteristics from images, substituting the mechanical teaching process with an automated information processing system that achieves the same goal without time loss.
3Adaptability or versatility
If the system handles a large number of different article types, then versatility is improved, but the complexity of model preparation and teaching operations increases
Solution Approach 1:
The system achieves universality by implementing a unified image processing and feature extraction framework that handles diverse article types through the same automated mechanisms. This multi-functional approach allows the system to process various articles without requiring separate preparation procedures, maintaining versatility while simplifying the overall system complexity.
Solution Approach 2:
The system manages complexity by dynamically adjusting recognition parameters and features based on the specific article being processed. Rather than maintaining fixed complex models for each article type, the system adapts parameters through automatic feature extraction, enabling versatile handling of different articles while keeping the system architecture relatively simple and manageable.
Data Source
AI summary
An article pickup device configured so as to select a first and second three-dimensional points present in the vicinity of each other based on position information of the plurality of three-dimensional points acquired by a three-dimensional measurement instrument and image data acquired by a camera, acquire an image gradient information in a partial image region including points on an image corresponding to these three-dimensional points, judge whether the first and second three-dimensional points are present on the same article based on a position information of the three-dimensional points and the image gradient information, and add the first and second three-dimensional points to the same connected set when it is judged that the first and second three-dimensional points are present on the same article.