3D Grid Container Fullness Tracking via Depth Sensor Point Clouds
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Solution Overview
Problem
Existing container fullness estimation methods are inaccurate under certain conditions, such as when items are hidden from view or when the container is partially obstructed, leading to inefficiencies in scheduling and resource allocation.
Innovation Solution
A method and system that generate a three-dimensional grid of cells representing the container interior, using a depth sensor to capture point clouds and update status indicators for each cell, allowing for accurate measurement and transmission of container fullness in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a depth sensor captures a single point cloud to estimate container fullness, then the measurement process is simple and fast, but the accuracy is insufficient when items are hidden or container is obstructed
Solution Approach 1:
The system performs preliminary actions by maintaining a three-dimensional grid model of the container interior and continuously updating cell status indicators before final fullness measurement. This preliminary modeling allows the system to track occupancy states proactively, improving measurement accuracy by anticipating changes in container fullness before they are fully visible to the depth sensor.
Solution Approach 2:
The system transitions from two-dimensional image analysis to three-dimensional point cloud processing with spatial depth information. By organizing the container interior into a three-dimensional grid of cells and tracking occupancy in 3D space, the system achieves more accurate fullness estimation that accounts for hidden items and obstructions that would be invisible in 2D projections.
2Measurement precision
If independent point cloud captures are used for fullness estimation, then processing is simpler, but accuracy deteriorates under occlusion conditions
Solution Approach 1:
The system maintains a persistent three-dimensional grid model that is continuously updated with new point cloud data. This preliminary modeling approach allows the system to carry forward occupancy information from previous measurements, so when occlusion occurs, the system can infer the presence of hidden items based on the established 3D model rather than relying solely on current visible data.
Solution Approach 2:
The system implements feedback by comparing new point cloud captures against the maintained three-dimensional grid model. Each new measurement feeds back into updating the cell status indicators, creating a closed-loop system that continuously refines the fullness estimation. This feedback mechanism allows the system to detect and compensate for occlusions by identifying discrepancies between expected and observed occupancy states.
3Measurement precision
If three-dimensional grid modeling with continuous updates is implemented, then fullness measurement accuracy improves, but computational complexity increases
Solution Approach 1:
The system segments the container interior into a three-dimensional grid of discrete cells, each with its own status indicator. This segmentation allows the system to process and update only the specific cells that are affected by new point cloud data rather than reprocessing the entire container volume. By dividing the problem into manageable cell-level units, the system achieves accurate real-time measurements while controlling computational energy consumption through localized updates.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach provides more accurate container fullness estimates even under conditions of occlusion or partial obstruction, enhancing the efficiency of scheduling and resource allocation processes.
Implementation Method 1
controlling a depth sensor to capture a sequence of point clouds, each point cloud depicting the container interior
Data Source
AI summary
A method includes: generating a three-dimensional grid of cells representing respective portions of an interior of a container, each cell having a status indicator defining an occupancy state of the corresponding portion of the container interior; during loading of the container: maintaining, for each of the cells, a current status indicator, controlling a depth sensor to capture a sequence of point clouds, each point cloud depicting the container interior, in response to each point cloud capture in the sequence, generating updated status indicators for the cells, based on (i) the point cloud, and on (ii) the current status indicators, replacing the current status indicators with the updated status indicators, measuring a current fullness of the container based on the current status indicators, and transmitting the current fullness to a computing device for at least one of display of the current fullness, or alert generation associated with the current fullness.