Occlusion Correction for Container Fullness Estimation
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Solution Overview
Problem
The logistics industry faces challenges in verifying the efficient loading of containers during the distribution process, as existing methods lack real-time monitoring and accurate fullness estimation, particularly due to occlusions that can lead to under or overestimation of container fullness.
Innovation Solution
A method and system utilizing a depth sensor to project a 2D grid map onto a shipping container, identifying and correcting occlusions through temporally proximate depth frames, and outputting corrected depth frames for fullness estimation, which includes detecting missing-data, moving, and discontinuous occlusions by analyzing clusters and depth value changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If real-time monitoring of container loading is implemented using depth sensors, then loading optimization and fullness estimation capability are improved, but measurement accuracy deteriorates due to occlusions causing under or overestimation
Solution Approach 1:
The system performs preliminary occlusion detection by analyzing depth frame clusters and identifying occlusion patterns before final fullness estimation is calculated. This allows the system to pre-correct measurement data, ensuring accurate fullness estimation while maintaining real-time monitoring capabilities for loading optimization
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring depth frames, detecting occlusions, and correcting fullness estimation in real-time. The correction process uses temporal analysis of multiple depth frames to identify and compensate for occlusion effects, feeding back improved measurement accuracy to the fullness estimation process while maintaining productivity
2Measurement precision
If occlusion detection and correction processes are added to the depth sensor system, then fullness estimation accuracy is improved, but system complexity increases
Solution Approach 1:
The system performs self-service by automatically detecting occlusions through depth frame cluster analysis and correcting its own measurements without external intervention. The occlusion detection and correction processes are integrated into the existing depth sensor workflow, allowing the system to maintain high measurement accuracy while avoiding the complexity of separate correction systems
Solution Approach 2:
The system changes parameters by analyzing temporal patterns in depth frame data and identifying occlusion-specific characteristics such as cluster density and depth value distributions. By detecting and correcting based on these parameter changes, the system improves fullness estimation accuracy while using computationally efficient methods that do not significantly increase system complexity
Data Source
AI summary
A method and apparatus for receiving a depth frame from a depth sensor oriented towards an open end of a shipping container, the depth frame comprising a plurality of grid elements that each have a respective depth value, identifying one or more occlusions in the depth frame, correcting the one or more occlusions in the depth frame using one or more temporally proximate depth frames, and outputting the corrected depth frame for fullness estimation.