Point-Cloud Trolley Detection for Accurate Pose Estimation
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Solution Overview
Problem
In the logistics and distribution field, it is challenging for unmanned carriage vehicles to accurately recognize the location and attitude of deployed transportation target objects, such as cage trucks, especially when they have complex outer shells like pipe frames and resin meshes, which makes it difficult to determine the presence, load amount, and packaging style.
Innovation Solution
The system employs a detection system that uses a combination of sensors, such as laser range finders, and data processing algorithms to generate point clouds from the detected outer shell, allowing for the fitting of an outer shape model to recognize the shape and estimate the location and attitude of the trolleys, even when they are loaded or unloaded, and adjusts the movement path of the unmanned carriage vehicle to deploy additional trolleys adjacent to existing ones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sensors and detection methods are used, then the system is simple, but the location and attitude of deployed trolleys cannot be accurately recognized
Solution Approach 1:
The detection system segments the detection task into multiple components: laser range finders capture distance information, image recognition algorithms process the captured data, and separate processing streams handle different aspects of trolley detection. This segmentation allows each component to be optimized independently while achieving high overall measurement precision for trolley location and attitude recognition.
Solution Approach 2:
The patent introduces an intermediary processing layer that bridges the raw sensor data and the final detection results. Image recognition algorithms act as intermediaries, transforming complex laser range finder data into interpretable trolley location and attitude information, thereby resolving the contradiction between measurement precision and system complexity.
2Productivity
If manual operation is used to deploy trolleys, then the system is simple to operate, but space utilization efficiency is low and automation is reduced
Solution Approach 1:
The unmanned carriage vehicle performs self-service by autonomously detecting trolley locations, calculating optimal deployment positions, and executing deployment operations without operator intervention. The vehicle uses its own sensors and processing capabilities to navigate and deploy trolleys, achieving high space utilization efficiency while eliminating the need for manual operation.
Solution Approach 2:
The system changes the operational parameters from manual control to automated control based on sensor data. By transforming the operation mode from human-driven to algorithm-driven, the system achieves improved productivity and space utilization efficiency while maintaining ease of operation through automated decision-making processes.
3Adaptability or versatility
If trolleys with complex outer shells are used, then the transportation capability is enhanced, but the detection and measurement difficulty increases
Solution Approach 1:
The detection system dynamically adapts to different trolley configurations by using flexible image recognition algorithms that can process various outer shell shapes. The laser range finders capture three-dimensional distance information that adapts to complex geometries, and the processing algorithms dynamically adjust to recognize trolleys regardless of their specific outer shell design, thereby maintaining detection capability while supporting versatile transportation capabilities.
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 enables accurate identification and positioning of trolleys, enhancing space utilization efficiency in pick-up locations and automating transportation operations, ensuring precise deployment and movement of trolleys without operator intervention.
Implementation Method 1
an acquisition portion that scans light to acquire point-cloud information corresponding to a plurality of positions of a detection target object
Data Source
AI summary
A detection system includes an acquisition portion scanning light to acquire point-cloud information corresponding to a plurality of positions of a detection target object; an estimation portion using consistency with an outer shape model of the detection target object to estimate a location and attitude of the detection target object based on the point-cloud information; and an output portion outputting information relating to a movement target location based on an estimation result, wherein the estimation portion fits an outer shape model indicating an outer shape of the detection target object to a point cloud according to the point-cloud information, and uses point-cloud information existing outside the outer shape model to estimate the location and the attitude of the detection target object.