3D Object Pose Estimation for Accurate Robotic Placement
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Solution Overview
Problem
Robots face challenges in accurately estimating the pose and shape of objects for efficient storage and shipping due to variations in lighting and surface reflections, leading to inaccurate packing and increased operational demands.
Innovation Solution
A system using multiple cameras from different viewpoints, combined with neural networks, performs image segmentation and 3D modeling to accurately determine the shape and pose of objects, enabling precise placement in containers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple cameras from different viewpoints are used, then measurement precision of shape and pose is improved, but device complexity increases
Solution Approach 1:
The system divides the imaging task into multiple segments by using multiple cameras positioned at different viewpoints. Each camera captures images from its specific perspective, and the results are segmented and processed separately before being integrated to form the complete 3D model. This segmentation allows each camera to focus on specific regions and reduces the computational burden on any single processing unit.
Solution Approach 2:
The patent transitions from 2D image processing to 3D spatial reasoning by capturing images from multiple viewpoints in different spatial dimensions. This dimensional change enables the system to reconstruct accurate 3D shapes and poses by integrating information across multiple perspectives, thereby improving measurement precision without requiring a single complex omnidirectional sensor.
2Productivity
If neural networks are used for object estimation, then productivity is improved, but measurement precision deteriorates due to overestimation or underestimation
Solution Approach 1:
The system implements feedback mechanisms where the neural network predictions are continuously compared against the actual 3D model measurements. Discrepancies between the estimated and actual values are fed back into the system to adjust and refine future predictions, gradually improving measurement precision while maintaining high productivity through automated processing.
Solution Approach 2:
The patent merges the strengths of neural networks (fast automated processing) with traditional computer vision algorithms (geometric accuracy). The neural networks perform initial rapid estimation, while geometric algorithms from multiple camera views provide correction and refinement. This combination achieves both high productivity and improved measurement precision by leveraging complementary strengths of different approaches.
3Device complexity
If inaccurate pose and shape estimates are used, then device complexity is reduced, but productivity deteriorates due to inefficient packing
Solution Approach 1:
The system performs preliminary accurate estimation of object pose and shape using multiple cameras and neural networks before the packing process begins. This preliminary action ensures that all necessary geometric information is captured and processed in advance, enabling efficient packing algorithms to optimize container space utilization without requiring complex real-time adjustments during the packing operation.
Data Source
AI summary
Systems and methods are described relating to pose and shape estimation of objects. In some examples, a camera generates images depicting an object from different viewpoints. Within the images, a system identifies corners of the object, and the system uses these corners to generate lines that are projected from different viewpoints through the corners. Points may be identified by intersecting lines such that the points may be used to generate estimations of the object. The estimation with the highest score may be used to place the object in a location different from where the object was located.