Aircraft Type Identification Using Virtual Lidar Point Clouds
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Solution Overview
Problem
Existing aircraft identification systems at airports struggle to reliably distinguish between different aircraft types and sub-types, particularly due to the limitations of video detection and guidance systems, and the high cost and data processing demands of 3D Lidar sensors, which affect the efficiency and safety of airfield operations.
Innovation Solution
A computing device simulates virtual Lidar sensor data for 3D models of aircraft types and sub-types to generate point clouds, using a classification model to identify and track aircraft, and determine their pose and location, without the need for expensive 3D Lidar sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If video detection and guidance systems are used for aircraft identification, then the system cost is reduced, but the ability to reliably distinguish between different aircraft types and sub-types deteriorates
Solution Approach 1:
The patent creates virtual 3D models of aircraft that replicate the geometric characteristics of real aircraft. These digital copies are used to generate synthetic Lidar point cloud data, enabling the system to identify aircraft types without requiring expensive physical Lidar sensors. The virtual models preserve the essential geometric features needed for classification while eliminating the need for costly hardware.
Solution Approach 2:
The patent replaces the mechanical/optical Lidar sensing system with a computational approach using virtual 3D models and synthetic data generation. Instead of using physical sensors to capture aircraft geometry, the system uses pre-created digital models and rendering techniques to generate equivalent measurement data, substituting hardware-based detection with software-based simulation.
2Measurement precision
If 3D Lidar sensors are used for aircraft identification, then the measurement precision of aircraft geometry is improved, but the device cost and data processing demands increase
Solution Approach 1:
The patent performs preliminary actions by pre-creating accurate 3D models of various aircraft types and generating their corresponding Lidar point cloud data in advance. This preparatory work allows the system to quickly compare real aircraft scans against pre-generated virtual data during actual identification operations, reducing real-time processing demands while maintaining high measurement precision.
Solution Approach 2:
The system creates virtual copies of Lidar point cloud data from 3D aircraft models. These synthetic point clouds serve as reference patterns for comparison with actual sensor data, enabling accurate aircraft identification without requiring the system to process complex raw Lidar data in real-time. The virtual copies preserve all geometric measurement precision while simplifying the identification process.
3Reliability
If virtual Lidar simulation and classification models are used, then the cost-effectiveness and reliability of aircraft identification is improved, but the computational complexity of the system increases
Solution Approach 1:
The classification model and virtual 3D models are prepared in advance during an offline training phase. This preliminary action allows the system to learn aircraft characteristics and generate reference data before actual identification operations begin. During real-time operation, the system only needs to perform comparisons against pre-computed data, significantly reducing computational complexity while maintaining high identification reliability.
Solution Approach 2:
The system generates and stores comprehensive virtual Lidar data for multiple aircraft types and configurations in advance, creating an extensive database of reference patterns. This excessive preparation ensures that all possible aircraft variations are covered, improving identification reliability without increasing real-time computational demands, as the heavy processing occurs during the offline data generation phase.
Data Source
AI summary
Methods, devices, and systems for aircraft identification are described herein. In some examples, one or more embodiments include a computing device comprising a memory and a processor to execute instructions stored in the memory to simulate virtual light detection and ranging (Lidar) sensor data for a three-dimensional (3D) model of an aircraft type to generate a first point cloud corresponding to the 3D model of the aircraft type, generate a classification model utilizing the simulated virtual Lidar sensor data of the 3D model of the aircraft type, and identify a type and/or sub-type of an incoming aircraft at an airport by receiving, from a Lidar sensor at the airport, Lidar sensor data for the incoming aircraft, generating a second point cloud corresponding to the incoming aircraft utilizing the Lidar sensor data for the incoming aircraft, and classifying the second point cloud corresponding to the incoming aircraft using the classification model.