Adaptive ROI Selection for Bin Picking Near Wall Objects
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Solution Overview
Problem
Current approaches to determining regions of interest (ROI) for robotic grasping in bin picking are inefficient, particularly when objects are proximate to bin walls, leading to suboptimal grasp point estimation and performance issues.
Innovation Solution
An autonomous system uses a depth camera and processor to generate an adaptive ROI by expanding the default ROI from the bin bottom, excluding bin walls and minimizing empty regions, allowing for accurate grasp point determination on objects near or against walls.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed default ROI is used for bin picking, then the system is simple to implement, but objects proximate to bin walls are excluded from grasp point determination
Solution Approach 1:
The patent implements a dynamic ROI that automatically adjusts its boundaries based on detected object positions. The system transitions from a static fixed ROI to a dynamic adaptive ROI that expands to include objects near bin walls while excluding actual wall regions, resolving the contradiction between adaptability and complexity
Solution Approach 2:
The system uses feedback from depth image analysis to determine object positions and adjust the ROI boundaries accordingly. By analyzing the depth data and detecting object-wall relationships, the system dynamically modifies the ROI to include relevant objects while maintaining computational efficiency
2Measurement precision
If the ROI is expanded to include all objects near walls, then grasp point accuracy improves, but computational load and processing time increase
Solution Approach 1:
The patent applies local quality by selectively expanding the ROI only in regions where objects are detected near bin walls, rather than uniformly expanding the entire ROI. This localized approach maintains high grasp point accuracy for relevant objects while minimizing unnecessary computational processing in empty regions
Solution Approach 2:
The system performs partial expansion of the ROI only where needed to include objects near walls, rather than expanding to cover the entire bin. This partial action approach achieves sufficient grasp point accuracy without the excessive computational burden of processing the complete bin area
3Area of stationary object
If bin walls are included in the ROI, then the region covers the entire bin area, but grasp computations are interfered with by wall artifacts
Solution Approach 1:
The patent extracts and excludes bin wall regions from the ROI by detecting wall boundaries in the depth image and creating a masked ROI that covers the bin interior while excluding wall areas. This extraction approach maintains comprehensive object coverage while eliminating harmful wall interference from grasp computations
Data Source
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AI summary
An autonomous system can include a depth camera configured to capture a depth image of a bin that contains a plurality of objects from a first direction, so as to define a captured image. Based on the bottom end of the bin and the captured image, the system can generate a cropped region that defines a plane along a second direction and a third direction that are both substantially perpendicular to the first direction. Based on the captured image, the system can make a determination as to whether at least one object of the plurality of objects lies outside the cropped region. Based on the determination, the system can select a final region of interest for determining grasp points on the plurality of objects.