Object Segmentation Using Visual Cues for Robotic Handling
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Solution Overview
Problem
Robotic systems face challenges in accurately segmenting and distinguishing objects, particularly when objects such as boxes are piled together, due to features like printed lines and creases that can be mistaken for boundaries, leading to incorrect object recognition.
Innovation Solution
The system employs image processing techniques to identify and utilize specific surface features like tape, barcodes, and stickers, which are less likely to be on boundaries or split between objects, to create a virtual representation of the environment, segmenting objects based on these features and adjusting image processing to enhance segmentation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If object segmentation is performed using all visible features in images, then complete object boundaries are captured, but false boundaries are created due to printed lines and creases being mistaken for object boundaries
Solution Approach 1:
The patent segments the image processing task into multiple stages: first identifying candidate boundaries from all visible features, then separately identifying object-specific visual cues (tape, barcodes, stickers), and finally combining these cues to validate and refine boundary detection. This multi-stage segmentation approach allows the system to distinguish between actual object boundaries and false boundaries caused by printed lines or creases.
Solution Approach 2:
The patent introduces object-specific visual cues (tape, barcodes, stickers) as intermediary elements that mediate between raw image features and final object boundary determination. These cues serve as reliable indicators that help disambiguate true boundaries from false ones, acting as a bridge between incomplete boundary detection and accurate object recognition.
2Productivity
If the system uses predetermined knowledge of object locations and types, then robotic manipulation is efficient, but the system cannot adapt to mixed environments with varying object types and weights
Solution Approach 1:
The patent makes the visual cue detection system universal by designing it to identify multiple types of object-specific features (tape, barcodes, stickers) across different object categories. This multi-functional detection capability allows the same system to handle homogeneous environments efficiently while also adapting to mixed environments with varying object types, weights, and configurations.
Solution Approach 2:
The patent enables adaptability by dynamically adjusting detection parameters based on the detected visual cues. When object-specific features like tape or barcodes are detected, the system modifies its boundary detection thresholds and confidence levels accordingly, allowing efficient manipulation in predetermined environments while adapting to unexpected object types in mixed environments.
3Measurement precision
If image processing focuses on detecting object boundaries, then segmentation is achieved, but confidence scores decrease due to false positives from non-boundary features
Solution Approach 1:
The patent extracts and isolates object-specific visual cues (tape, barcodes, stickers) from the general set of image features. By separating these reliable boundary indicators from false boundary features like printed lines and creases, the system can focus boundary detection on high-confidence features only, thereby maintaining segmentation accuracy while preserving confidence score integrity.
Solution Approach 2:
The patent implements a feedback mechanism where detected object-specific visual cues feed back into the boundary detection process to adjust confidence scores. When visual cues confirm a boundary location, the system increases confidence; when visual cues are absent or contradictory, the system reduces confidence and re-evaluates potential boundaries, thereby maintaining accurate confidence scoring despite the presence of false positive features.
Data Source
AI summary
One or more images of a physical environment may be received, where the one or more images may include one or more objects. A type of surface feature predicted to be contained on a portion of one or more surfaces of a single object may be determined. Surface features of the type within regions of the one or more images may then be identified. The regions may then be associated to corresponding objects in the physical environment based on the identified surface features. Based at least in part on the regions associated to the corresponding objects, a virtual representation of the physical environment may be determined, the representation including at least one distinct object segmented from a remaining portion of the physical environment so as to virtually distinguish a boundary of the at least one distinct object from boundaries of objects present in the remaining portion of the physical environment.