3D Sensor Element Detection for Autonomous Warehouse Alignment
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Solution Overview
Problem
Current methods for detecting elements in environments, such as warehouses, using autonomous vehicles are inefficient due to the inability to accurately identify and filter image data from 3D sensors, leading to difficulties in recognizing and aligning elements like pallets and racks for stacking or placement.
Innovation Solution
The method involves using a 3D camera on an autonomous vehicle to capture image data, filter it to reduce data complexity, and apply a deterministic process to identify components by generating clusters and analyzing their connectivity, allowing for precise detection of elements based on predefined conditions and attributes stored in a library.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image data from 3D sensors is captured to detect elements in the environment, then detection capability is improved, but data complexity and processing difficulty increase
Solution Approach 1:
The patent segments the complex image data processing into distinct stages: capturing raw image data from 3D sensors, filtering the data to remove noise and irrelevant information, and then processing the filtered data to identify element components. This segmentation reduces the complexity of handling raw data while maintaining detection precision.
Solution Approach 2:
The patent extracts only the relevant features from the complex image data by applying filters that remove noise and unnecessary information. The filtering process extracts essential structural features of elements while discarding redundant data, thereby reducing data complexity while preserving detection capability.
2Productivity
If filtering is applied to reduce data amount, then processing efficiency is improved, but information loss may occur
Solution Approach 1:
The filtering process applies different processing qualities to different parts of the data. Essential structural features are preserved with high fidelity, while noise and irrelevant details are removed. This local differentiation ensures that important information is retained while unnecessary data is eliminated, balancing efficiency and information preservation.
3Measurement precision
If deterministic processes are used to identify components, then identification accuracy is improved, but computational requirements increase
Solution Approach 1:
The patent performs preliminary filtering of image data before applying deterministic identification processes. By pre-processing the data to remove noise and organize relevant features, the subsequent deterministic analysis requires fewer computational resources while maintaining high identification accuracy. The preliminary action reduces the burden on the main processing stage.
Data Source
AI summary
An example method of detecting an element using an autonomous vehicle includes the following operations: using a sensor on the autonomous vehicle to capture image data in a region of interest containing the element, where the image data represents components of the element; filtering the image data to produce filtered data having less of an amount of data than the image data; identifying the components of the element by analyzing the filtered data using a deterministic process; and detecting the element based on the components.