Robot Object Segmentation Using Template-Constrained Affine Matching
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Solution Overview
Problem
Existing object segmentation methods, particularly for transparent objects, face challenges due to limited training data, occlusions, and environmental factors, leading to inaccurate segmentation and increased computational burden, especially in robotic applications like bin picking and navigation.
Innovation Solution
A few-shot learning methodology using a Mask-RCNN backbone for object segmentation, combined with constrained affine transformations based on object properties, reduces the need for extensive training data by leveraging object symmetry and rigidity, and improves segmentation confidence through template comparisons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional object segmentation methods (Mask R-CNN, U-Net, DeepLab) are used for transparent objects, then segmentation can be performed, but accuracy deteriorates due to lack of surface features, occlusions, and limited training data
Solution Approach 1:
The patent introduces an intermediary representation called 'object blueprint' that captures the essential geometric and structural properties of objects. This blueprint serves as a mediator between the input image and segmentation output, enabling accurate segmentation of transparent objects by comparing their structural signatures rather than relying on visual appearance alone.
Solution Approach 2:
The patent replaces traditional deep learning-based mechanical segmentation systems with a geometry-aware approach using signed distance functions and structural signatures. This substitution allows the system to handle transparent objects effectively by focusing on geometric invariants rather than pixel-based patterns that fail for transparent surfaces.
2Difficulty of detecting and measuring
If deep learning-based segmentation is used, then object detection capability is improved, but computational burden increases and performance deteriorates with limited training data
Solution Approach 1:
The patent extracts only the essential geometric features needed for segmentation by computing signed distance functions and structural signatures. This extraction approach eliminates the need for complex deep learning models while retaining the core detection capability, significantly reducing computational requirements.
Solution Approach 2:
The patent creates simplified copies of objects in the form of geometric blueprints and structural signatures. These copies capture the essential characteristics needed for segmentation without requiring the full complexity of original deep learning models, enabling efficient processing with limited data.
3Measurement precision
If segmentation is performed on transparent objects with occlusions and overlapping instances, then complete object detection is attempted, but segmentation accuracy deteriorates due to ambiguous boundaries and lack of visual features
Solution Approach 1:
The patent transitions from 2D image space to a geometric feature space by computing signed distance functions and structural signatures. This dimensional transformation allows the system to distinguish overlapping transparent objects by their geometric properties rather than relying on 2D visual features that are ambiguous for transparent surfaces.
Solution Approach 2:
The patent transforms the representation of objects from visual appearance (color, texture) to geometric properties (signed distance values, structural signatures). This transformation is analogous to changing from color space to a different feature space where transparent objects can be distinguished by their geometric invariants rather than visual appearance.
Data Source
AI summary
Embodiments disclosing a controller for controlling a robot to perform a task are provided. The task is performed in an environment that is represented by an input image. The controller causes segmenting of an object in the input image. A confidence level of segmentation is updated by comparing the segmented object with constrained affined transformations of a template of the object. The constrained affine transformations are based on constraints indicative of a property of the object. The property of the object and the updated confidence level of segmentation are then used for performing the task.