3D Warehouse Reconstruction for Occluded Inventory Counting
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Solution Overview
Problem
Existing inventory management systems struggle with accurately counting visible and occluded objects in warehouse environments, often requiring manual intervention and leading to inefficiencies and human errors.
Innovation Solution
Utilizing autonomous drones equipped with sensors and machine-learned models to generate three-dimensional semantic reconstructions of warehouse environments, enabling detection and counting of visible and occluded objects without human intervention, through a combination of semantic fusion and combinatorial optimization models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual inventory counting methods are used, then human intervention can handle complex object arrangements, but productivity is reduced and human errors increase
Solution Approach 1:
The patent replaces manual mechanical counting methods with an autonomous robotic system equipped with sensors and machine learning models. The robotic platform autonomously navigates the warehouse, captures images of inventory objects, and uses computer vision algorithms to detect and count objects, thereby eliminating human intervention while improving both accuracy and productivity.
Solution Approach 2:
The system enables self-service inventory management by autonomously performing object detection and counting without human assistance. The robotic platform independently navigates, captures images, processes data through machine learning models, and generates inventory counts, making the system self-sufficient and eliminating dependency on manual labor.
2Productivity
If heavy machinery is deployed for inventory management, then large-scale object handling is possible, but device complexity and operational risk increase
Solution Approach 1:
The patent replaces heavy machinery with a lightweight robotic platform that uses sensors and computational algorithms instead of mechanical force. The system captures images of inventory objects and uses machine learning models to detect and count them, eliminating the need for physical manipulation by heavy equipment while reducing device complexity and operational risk.
Solution Approach 2:
The patent introduces an intermediary computational layer between the physical inventory objects and the counting process. Machine learning models and computer vision algorithms serve as intermediaries that process visual data and infer object counts without requiring direct physical interaction, thereby simplifying the system and reducing complexity.
3Measurement precision
If traditional sensor-based detection is used, then visible objects can be detected, but occluded objects remain undetected
Solution Approach 1:
The patent transitions from two-dimensional image analysis to three-dimensional semantic reconstruction. By building 3D models of the warehouse environment and inventory objects, the system can infer the presence and location of occluded objects based on spatial relationships and visible object configurations, thereby recovering information that would be lost in traditional 2D detection methods.
Solution Approach 2:
The patent performs preliminary 3D semantic reconstruction of the warehouse environment before conducting object detection. This preliminary action creates a comprehensive spatial model that enables the system to predict and detect occluded objects by understanding the three-dimensional layout and relationships between objects, even before directly observing them.
Data Source
AI summary
A system and method for detecting and counting objects in a warehouse environment is disclosed. The system may receive sensor data indicative of a warehouse environment. The method includes generating, based on the sensor data, a reconstruction of the warehouse environment, The reconstruction can include one or more slots comprising a plurality of objects. The method includes determining, based on the reconstruction, a number of occluded objects. The method includes determining, based on the reconstruction and the number of occluded objects, a total number of objects within each slot of the one or more slots.