Imaging Controller Correcting Translucency Artifacts in Depth Maps
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Solution Overview
Problem
In retail environments, automated detection of product status issues on shelves is hindered by changing conditions such as variable lighting, obstructions, and the presence of translucent or transparent objects, leading to inaccurate depth measurements and labor-intensive manual corrections.
Innovation Solution
A mobile automation system equipped with depth sensors and an imaging controller that corrects translucency artifacts by obtaining depth measurements, defining shelf edges, generating a depth map, detecting upper object boundaries, and updating nearest object depths to produce a corrected depth map, thereby improving the accuracy of product status detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If depth sensors are used to detect objects on shelves, then automation level increases, but measurement precision deteriorates due to translucency artifacts
Solution Approach 1:
The patent introduces an imaging controller as an intermediary component that processes depth measurements from the depth sensor. This controller applies correction algorithms to compensate for translucency artifacts, thereby maintaining automation while improving measurement precision. The intermediary processing layer bridges the gap between automated sensing and accurate measurement.
Solution Approach 2:
The system changes the parameter being measured by not just relying on raw depth values but by computing corrected depth values that account for translucency. The imaging controller modifies the depth measurement parameter through mathematical corrections based on image data, transforming inaccurate measurements into accurate ones while preserving automation.
2Measurement precision
If manual assessment is used to correct detection errors, then measurement precision improves, but productivity decreases due to labor-intensive processes
Solution Approach 1:
The system implements self-service by enabling automated detection and correction of product status issues without requiring manual intervention. The imaging controller automatically processes depth measurements, corrects translucency artifacts, and identifies product status, thereby maintaining high measurement precision while significantly improving productivity through autonomous operation.
Solution Approach 2:
The system uses feedback mechanisms where the imaging controller continuously processes depth measurements and image data, compares detected objects with expected shelf configurations, and automatically adjusts detections to correct errors. This closed-loop feedback system maintains accuracy while eliminating manual assessment, thereby improving productivity.
3Adaptability or versatility
If variable lighting conditions are present in the environment, then adaptability increases, but measurement precision deteriorates due to imaging variations
Solution Approach 1:
The imaging controller performs multiple functions: it processes depth measurements, corrects translucency artifacts, compensates for lighting variations, and identifies product status. This multi-functional approach allows the system to adapt to variable lighting conditions while maintaining measurement precision through integrated image and depth data processing.
Solution Approach 2:
The system uses composite data processing by combining depth measurements with image data from cameras. This composite approach allows the imaging controller to compensate for lighting variations by cross-referencing information from multiple sensing modalities, thereby maintaining precision across varying environmental conditions.
Data Source
AI summary
A method in an imaging controller of correcting translucency artifacts in data representing one or more objects disposed on a shelf includes: obtaining a plurality of depth measurements captured by a depth sensor and corresponding to an area containing the shelf; obtaining (i) a definition of a plane containing edges of the shelf, (ii) a location in the plane of an upper shelf edge, and (iii) a location in the plane of a lower shelf edge adjacent to the upper shelf edge; generating a depth map containing, for each of a plurality of positions in the plane, a nearest object depth; detecting an upper object boundary in the depth map between the upper and lower support surface edges; updating each nearest object depth between the upper object boundary and the lower shelf edge to contain a depth of the upper object boundary; and storing the corrected depth map.