3D Point Cloud Localization of Horizontal Infrastructure for AMRs
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Solution Overview
Problem
Existing approaches for automated detection and localization of horizontal infrastructure by Autonomous Mobile Robots (AMRs) are challenging due to variations in infrastructure size and material, and the dynamic nature of the physical environment.
Innovation Solution
A robotic vehicle equipped with a navigation system, payload engagement apparatus, and one or more 3D sensors that collect point cloud data to perform an infrastructure localization analysis, determining if the infrastructure matches a modeled type and if its horizontal surface is obstruction-free.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated detection and localization is performed using existing approaches, then payload delivery can be executed, but accuracy and safety are compromised due to infrastructure variations and environmental dynamics
Solution Approach 1:
The patent transitions from 2D camera-based detection to 3D point cloud analysis using LiDAR. This dimensional change enables accurate measurement of infrastructure geometry, surface orientation, and obstruction detection in three-dimensional space, directly resolving the localization accuracy problem while maintaining reliability
Solution Approach 2:
The system changes the detection parameters by using multiple LiDAR sensors positioned at different locations on the robotic vehicle. This multi-sensor approach captures infrastructure from multiple angles and distances, enabling precise 3D reconstruction and accurate localization despite infrastructure variations and environmental dynamics
2Measurement precision
If 3D sensors and point cloud analysis are used to improve localization accuracy, then measurement precision improves, but device complexity increases
Solution Approach 1:
The LiDAR sensors serve multiple functions: they detect infrastructure geometry for localization, measure surface orientation for payload alignment, identify obstructions for safety, and characterize infrastructure material properties. This multi-functionality reduces the need for separate sensors for each task, mitigating the complexity increase
Solution Approach 2:
The system creates a digital 3D copy (point cloud model) of the physical infrastructure. This digital replica can be analyzed, stored, and processed without requiring physical contact or additional sensors, enabling precise localization and obstruction detection while keeping the physical sensor system manageable
3Reliability
If point cloud data is collected and processed to verify obstruction-free surfaces, then payload delivery safety improves, but processing time and computational requirements increase
Solution Approach 1:
The system performs preliminary 3D scanning and point cloud generation as the robotic vehicle approaches the infrastructure. This advance detection allows the processing system to analyze the data, identify obstructions, and plan the payload delivery path before actual delivery, ensuring safety without delaying the operation
Solution Approach 2:
The point cloud data processing is segmented into distinct analytical tasks: geometry extraction, surface orientation calculation, obstruction identification, and material classification. This segmentation allows parallel processing of different aspects of the data, reducing overall processing time while maintaining comprehensive safety verification
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
Enables safe and accurate payload delivery onto horizontal infrastructure by accurately localizing the infrastructure and verifying the absence of obstructions, thereby improving the reliability of AMR operations in dynamic environments.
Implementation Method 1
one or more 3D sensors configured to collect point cloud data
Data Source
AI summary
In accordance with one aspect of the inventive concepts, provided is a method and a system for horizontal infrastructure localization, comprising a robotic vehicle platform, such as an AMR, a mechanism for collecting sensor data, e.g., point cloud data, such as a LiDAR scanner or 3D camera, and a processor configured to process the sensor data to identify and determine a position and orientation of the horizontal infrastructure. The robotic vehicle processes the sensor data to identify a horizontal infrastructure, determine its pose, and then determine if an area of a horizontal surface of the horizontal infrastructure is clear for dropping a load, e.g., palletized load.