Forklift Stereoscopic Camera Positioning
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Solution Overview
Problem
Current technologies for tracking vehicle positions in warehouse environments are prone to errors and inefficiencies, particularly due to reliance on operator data entry, which can lead to misplaced or lost pallets due to inaccurate location recording.
Innovation Solution
A computer-implemented method using stereoscopic image data from cameras affixed to vehicles, such as forklifts, to recognize objects and determine their locations within a spatial model, allowing for accurate vehicle positioning without continuous network connections, and reducing bandwidth usage by providing preprocessed image data at less frequent intervals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If operator data entry is used to track vehicle positions, then the system is simple to operate, but the location accuracy deteriorates leading to misplaced or lost pallets
Solution Approach 1:
The forklift vehicle performs self-location by capturing images with its onboard camera and processing them to determine its own position in the warehouse. The vehicle uses its own sensors and computational resources to independently determine location without requiring external operator intervention or manual data entry, thereby maintaining ease of operation while dramatically improving location accuracy.
Solution Approach 2:
The manual mechanical process of operator data entry is replaced with an automated optical-mechanical system. The camera captures images of warehouse features, and computational algorithms automatically process these images to determine vehicle position, substituting human manual input with automated image processing to achieve both ease of operation and high measurement precision.
2Measurement precision
If continuous image data is transmitted to determine vehicle location, then location accuracy is improved, but network bandwidth consumption increases
Solution Approach 1:
The forklift performs preliminary image processing and location determination locally using its onboard computational resources before transmitting any data. By pre-processing images to extract only essential location information and filtering out redundant data, the system reduces the amount of data that needs to be transmitted over the network, thereby maintaining location accuracy while minimizing bandwidth consumption.
Solution Approach 2:
The system extracts only the essential location information from the full image data and transmits only this extracted information to remote systems. By taking out and transmitting only the necessary data components rather than the complete continuous image streams, the system maintains the accuracy needed for location determination while significantly reducing network bandwidth requirements.
3Measurement precision
If real-time image processing is performed to track vehicle position, then location accuracy is improved, but processing time increases
Solution Approach 1:
The image processing task is segmented into distinct functional stages: image capture, feature detection, location determination, and data transmission. By dividing the processing workload into separate segments that can be executed in parallel or with optimized sequencing, the system reduces overall processing time while maintaining location accuracy through focused computational efforts on each specific segment.
Solution Approach 2:
The system performs partial image processing by focusing computational resources only on the specific regions and features necessary for location determination, rather than processing every pixel and detail in the entire image. This partial action approach maintains sufficient location accuracy while significantly reducing processing time by avoiding excessive computation on unnecessary data.
Data Source
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AI summary
This specification generally discloses technology for tracking vehicle positions in a warehouse environment. A system receives stereoscopic image data from a camera on a forklift, in some implementations. The system recognizes an object that is represented in the stereoscopic image data, identifies a representation of the recognized object in a spatial model that identifies, for each of a plurality of objects in an environment, a corresponding location of the object in the environment, determines the location of the recognized object in the environment, determines a relative position between the forklift and the recognized object, based on a portion of the received stereoscopic image data that represents the recognized object, and determines a location of the forklift in the environment, based on the determined location of the recognized object in the environment, and the determined relative position between the forklift and the recognized object.