Edge-Based Object Pose Refinement for Robotic Package Handling
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Solution Overview
Problem
Robotic systems face inaccuracies in detecting the position and orientation of objects due to errors in surface markings and designs, leading to potential mishandling and increased risk of injury, especially when handling heavy or lop-sided packages.
Innovation Solution
Implementing a pairwise edge analysis to identify and correct offsets and alignment errors between initial object estimations and detected features, refining object detection results using 2D and 3D image data to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If surface marking and design matching is used for object detection, then detection speed is improved, but measurement precision deteriorates due to errors in surface markings
Solution Approach 1:
The patent divides the detection process into two independent stages: initial rapid detection using surface marking matching, followed by refinement using geometric feature detection. This segmentation allows each stage to optimize for its specific function - speed in the first stage and precision in the second - thereby resolving the contradiction between detection speed and measurement precision.
Solution Approach 2:
The system performs preliminary detection using surface markings to quickly identify objects and their approximate positions. This preliminary action enables the system to then focus computational resources on refining the position and orientation of detected objects using more precise geometric feature analysis, thus achieving both speed and accuracy.
2Measurement precision
If manual registration is performed for packages with detection errors, then measurement precision is improved, but productivity deteriorates due to stoppages and manual intervention
Solution Approach 1:
The system implements self-service by automatically detecting and correcting its own detection errors through the refinement process. When initial detection based on surface markings produces inaccurate results, the system autonomously performs additional geometric feature analysis to correct the position and orientation without requiring manual intervention, thereby maintaining both precision and productivity.
Solution Approach 2:
The patent incorporates feedback mechanisms where detection results are continuously evaluated and refined. The system uses feedback from geometric feature detection to correct errors in initial surface marking-based detection, creating a closed-loop system that automatically improves measurement precision without stopping the de-palletizing process.
3Measurement precision
If comprehensive geometric feature analysis is performed, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The detection system operates dynamically by adjusting its analysis depth based on initial detection confidence. When surface marking matching yields high confidence results, the system performs minimal additional analysis. When confidence is low or errors are detected, the system automatically activates more comprehensive geometric feature analysis, thereby achieving high precision without always requiring full system complexity.
Solution Approach 2:
The system applies different levels of detection complexity to different objects or detection scenarios. For objects with clear, distinctive surface markings, simpler detection suffices. For objects where precision is critical or initial detection is uncertain, the system locally applies more comprehensive geometric feature analysis, optimizing the balance between precision and complexity on a case-by-case basis.
Data Source
AI summary
The present disclosure relates to verifying an initial object estimation of an object. A two-dimensional (2D) image representative of an environment including one or more objects may be obtained. The 2D image may be inspected to detect edges of an object. The edges may be processed to verify or update an initial object estimation to increase the accuracy of an object detection result.