Pseudo-Image Pose Estimation for Stacked Object Alignment
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Solution Overview
Problem
Material handling equipment faces challenges in accurately aligning stacked objects due to external environments and equipment errors, affecting operation safety during the stacking process.
Innovation Solution
A method involving sensors to acquire target point clouds of objects, generate pseudo images, determine relative pose data, and compare it with a threshold to align stacking objects, with dynamic adjustment of the equipment's pose to ensure accurate alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If material handling equipment stacks objects based on basic positioning, then stacking speed is maintained, but alignment accuracy deteriorates due to external environment and equipment errors
Solution Approach 1:
The system acquires point cloud data of the stacked object and calculates relative pose data to determine alignment state, providing real-time feedback on positioning accuracy. This feedback mechanism enables dynamic adjustment of the equipment pose to compensate for external environment disturbances and equipment errors, ensuring reliable operation safety while maintaining high alignment accuracy
Solution Approach 2:
The patent replaces traditional mechanical positioning systems with a vision-based point cloud processing system. By using sensors to acquire point cloud data and computational algorithms to determine alignment state, the system achieves higher measurement precision without relying on mechanical precision, thereby improving alignment accuracy while maintaining operation safety
2Measurement precision
If traditional alignment methods are used, then equipment complexity is low, but alignment accuracy deteriorates under external environmental influences
Solution Approach 1:
The system replaces complex mechanical alignment mechanisms with a computational vision system that processes point cloud data. This substitution achieves high alignment accuracy through software-based pose estimation and alignment state determination, avoiding the need for complex mechanical adjustment mechanisms while maintaining low device complexity
Solution Approach 2:
The system creates a digital copy of the physical stacking scenario through point cloud data acquisition. By working with this digital representation to calculate relative pose data and determine alignment state, the system achieves precise alignment measurements without requiring complex physical measurement devices, thereby improving alignment accuracy while keeping device complexity low
3Measurement precision
If dynamic pose adjustment is implemented, then alignment accuracy is improved, but productivity decreases due to additional adjustment time
Solution Approach 1:
The system performs alignment checks and pose adjustments only when necessary, based on the determined alignment state. By avoiding unnecessary full adjustment cycles and performing partial adjustments when minor corrections are needed, the system maintains high alignment accuracy while minimizing the time impact on stacking speed, thus preserving productivity
Data Source
AI summary
Disclosed are a method for determining an alignment state, a controller, and material handling equipment. A main technical solution includes: acquiring, by using a sensor, a target point cloud of a first stacking object and a target point cloud of a second stacking object; determining, based on the target point cloud of the first stacking object, a first pseudo image corresponding to the first stacking object; determining, based on the target point cloud of the second stacking object, a second pseudo image corresponding to the second stacking object; determining relative pose data between the first stacking object and the second stacking object based on the first pseudo image and the second pseudo image; and comparing the relative pose data with a threshold to determine an alignment state between the first stacking object and the second stacking object.