3D Point Cloud Object Positioning for Accurate Pallet Identification
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Solution Overview
Problem
Existing pallet identification and positioning systems for automated forklifts face challenges in accuracy due to reliance on 2D imaging and single-camera setups, which can lead to significant errors in identifying and positioning pallets.
Innovation Solution
An object positioning method and system that utilize 3D point cloud data from a sensing device, extract key points, and input surrounding area data and a preset feature descriptor into a neural network to calculate scene feature descriptors, perform feature matching, and accurately calculate the position of the target object in actual space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single camera is used for positioning, then the device complexity is reduced, but the positioning accuracy deteriorates due to significant errors
Solution Approach 1:
The patent transitions from 2D image data to 3D point cloud data for object positioning. By using depth information from the sensing device to generate three-dimensional point clouds, the system achieves accurate positioning without requiring multiple cameras, thus maintaining device simplicity while improving positioning precision.
2Measurement precision
If dual cameras are used for positioning, then the positioning accuracy is improved, but the device complexity and calculation amount increase
Solution Approach 1:
Instead of using multiple cameras to achieve 3D positioning, the patent employs a single sensing device that directly captures 3D point cloud data. This dimensional transition allows the system to achieve accurate positioning with reduced device complexity and lower computational requirements for parallax calculations.
Solution Approach 2:
The patent replaces the mechanical/optical system of multiple cameras with a sensing device that directly generates 3D point clouds. This substitution eliminates the need for complex multi-camera calibration and parallax-based geometric calculations, simplifying the system while maintaining positioning accuracy.
3Device complexity
If conventional 2D machine vision methods are used, then the device complexity is reduced, but the positioning accuracy deteriorates
Solution Approach 1:
The patent advances from 2D machine vision to 3D point cloud processing. By utilizing depth information and three-dimensional spatial coordinates, the system achieves accurate object identification and positioning while maintaining relatively simple device architecture, overcoming the limitations of traditional 2D methods.
Data Source
AI summary
An object positioning system and an object positioning method are provided. The object positioning system includes a sensing device, a storage device and a processing device. The sensing device collects point cloud data obtained from a scene including a target object. The processing device inputs surrounding area data centered on a key point and a preset feature descriptor to a neural network to calculate a scene feature descriptor of the scene. The processing device performs feature matching between the scene feature descriptor and the preset feature descriptor, and calculates a position of the target object in an actual space. The invention utilizes the feature extraction capability of the neural network to effectively improve the accuracy and stability of target object identification and positioning.