Monocular Depth Prediction for Translucent-Band Stow Occupancy
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Solution Overview
Problem
Conventional automated item stow systems fail to reliably and efficiently utilize available storage space in inventory holders due to the partial occlusion caused by translucent bands.
Innovation Solution
Implement monocular depth prediction and occupancy correction systems using machine learning models trained on imaging data with and without translucent bands to generate accurate depth maps and occupancy masks, correcting for perspective and occlusion errors to determine available storage space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional automated item stow systems use standard imaging methods, then the system structure remains simple, but the measurement precision of available storage space deteriorates due to partial occlusion by translucent bands
Solution Approach 1:
The patent introduces an intermediary processing pipeline between the imaging device and the storage space determination system. This pipeline includes monocular depth prediction models and occupancy correction models that act as mediators to interpret the imaging data, correct occlusion errors caused by translucent bands, and accurately determine available storage space without requiring complex multi-camera setups
Solution Approach 2:
The patent replaces complex mechanical imaging systems (such as multiple cameras or depth sensors) with computational methods. By using monocular depth prediction and occupancy correction algorithms, the system achieves accurate three-dimensional storage space determination from single-view images, substituting hardware complexity with software-based computational intelligence
2Reliability
If conventional systems do not correct for occlusion errors, then the system operation remains simple, but the reliability of storage space determination deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the occupancy correction model uses predicted depth information to identify and correct occlusion errors in the occupancy mask. The system continuously refines the available storage space determination by comparing initial occupancy assessments with depth-corrected assessments, ensuring reliable results even in the presence of translucent band occlusions
Solution Approach 2:
The patent performs preliminary depth prediction and occlusion correction before final storage space determination. By pre-processing the imaging data to correct for translucent band occlusions and generate accurate depth maps in advance, the system ensures reliable storage space assessment without adding complexity to the core stow operation
3Productivity
If the system uses accurate depth prediction and occupancy correction, then the productivity of item stow processes improves, but the use of energy increases due to complex processing
Solution Approach 1:
The patent applies partial action by selectively processing only the necessary portions of imaging data for depth prediction and occupancy correction. Rather than performing exhaustive analysis on entire inventory holder volumes, the system focuses computational resources on regions containing items or potential storage spaces, improving productivity while controlling energy consumption
Data Source
AI summary
Monocular depth prediction machine learning models may be trained to receive imaging data of inventory holders having translucent bands that at least partially occlude portions of compartments and items of the inventory holders. The monocular depth prediction models may process the imaging data to generate accurate and reliable depth maps for the inventory holders, while substantially ignoring or disregarding the translucent, partially occlusive bands. Further, the depth maps may be utilized by various occupancy correction models to improve determinations related to available storage space within compartments of the inventory holders, thereby improving the accuracy and efficiency of various material handling processes.