Edge-Based Object Pose Refinement for De-Palletizing Robots
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Solution Overview
Problem
Robotic systems face inaccuracies in detecting the position and orientation of objects on pallets due to errors in computer vision and manufacturing defects, leading to potential mishandling and increased risk of injury to human workers during de-palletization.
Innovation Solution
Implementing a post-detection refinement mechanism that uses pairwise edge analysis to update and verify initial object estimations based on 2D and 3D image data, comparing detected edges and corners to improve accuracy and alignment with actual object positions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If initial object estimation is performed using standard computer vision techniques, then detection speed is maintained, but measurement precision deteriorates due to errors in detecting position and orientation
Solution Approach 1:
The detection process is divided into two distinct phases: initial object estimation using standard computer vision techniques, and post-detection refinement using edge-based verification. This segmentation allows each phase to specialize in its strength while maintaining overall system efficiency.
Solution Approach 2:
The system performs preliminary detection using fast but less accurate methods, then applies refinement actions based on detected edges and corners. This preliminary action approach ensures that most detections are completed quickly while only requiring detailed refinement for specific cases.
2Measurement precision
If post-detection refinement using edge analysis is implemented, then measurement precision improves, but processing time increases
Solution Approach 1:
The refinement process applies edge-based verification selectively rather than to all detected objects. By focusing computational resources on verifying edges and corners only where needed, the system achieves high precision without uniformly increasing processing time for every detection.
Solution Approach 2:
The system replaces purely mechanical/image-based detection with a hybrid approach that incorporates geometric reasoning through edge and corner analysis. This substitution allows for more accurate position and orientation determination without requiring additional physical sensors or complex hardware.
3Reliability
If accurate object detection is performed to prevent mishandling, then reliability improves, but device complexity increases due to additional verification mechanisms
Solution Approach 1:
The system uses detected edges and corners to provide feedback on the accuracy of initial object estimations. This feedback mechanism allows the system to verify and correct detection results, ensuring reliable handling decisions without requiring overly complex verification infrastructure.
Solution Approach 2:
Edges and corners serve as intermediary geometric features that bridge the gap between raw image data and final object parameter estimation. These intermediaries provide a structured way to verify detection accuracy without directly complicating the overall system architecture.
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.