Drone Sensor Data Fusion in a 3D Virtual Map
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Solution Overview
Problem
Current systems lack an efficient method to integrate and visualize sensor data from drones in real-time across large areas, particularly in emergency and disaster scenarios, where rapid data collection and analysis are crucial.
Innovation Solution
A system that utilizes 3-D visualization software to create a virtual environment, where geometry data of the real-world landscape is used to project sensor data from drones onto a map, allowing for real-time fusion and display of information collected by multiple drones, enabling systematic navigation and visualization of large areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sensor data from multiple drones is collected and integrated in real-time, then data collection efficiency and coverage area are improved, but system complexity and computational requirements increase
Solution Approach 1:
The patent introduces a server as an intermediary component that receives sensor data from multiple drones, performs fusion processing, and generates integrated visualizations. This mediator architecture allows drones to operate independently while the server handles the complex integration tasks, resolving the contradiction between improved data collection efficiency and increased system complexity by centralizing computational requirements.
Solution Approach 2:
The system creates virtual copies of the physical environment by generating three-dimensional visualizations and digital twins of the area being monitored. These virtual representations allow complex sensor data from multiple drones to be integrated and displayed in an simplified visual format, improving data collection efficiency while presenting the processed information in a manageable form that reduces perceived system complexity.
2Loss of information
If real-time sensor data fusion is performed across large areas, then visualization comprehensiveness is improved, but processing time and computational resources increase
Solution Approach 1:
The patent divides the large monitoring area into multiple zones or regions, with each drone responsible for collecting data from a specific segment. The server then integrates these segmented data sets to create a comprehensive whole. This segmentation approach allows real-time processing of large areas by breaking down the computational task into manageable pieces, maintaining visualization comprehensiveness while reducing overall processing time through parallel processing.
Solution Approach 2:
The system performs preliminary actions by pre-processing sensor data as it is received from drones, including calibration, filtering, and initial integration, before generating the final three-dimensional visualization. This preliminary processing reduces the computational burden during real-time display operations, enabling comprehensive visualization of large areas while minimizing additional processing time delays.
3Reliability
If multiple sensors and drones are coordinated, then data accuracy and reliability are improved, but coordination complexity and operational difficulty increase
Solution Approach 1:
The patent implements a universal server platform that can interface with multiple types of sensors and drones through standardized protocols. This multi-functional system handles diverse data sources uniformly, improving data accuracy through integrated processing while simplifying coordination by providing a single point of control that manages all sensors and drones regardless of their specific types or capabilities.
Data Source
AI summary
Systems and methods for fusing sensor data from drones in a virtual environment are described In one embodiment, a method for fusing sensor data from drones in a virtual environment includes obtaining geometry data describing a real-world landscape, drawing a map within a virtual environment using a 3-D visualization software and the geometry data, placing a plurality of projectors on the map within the virtual environment corresponding to sensors in the region of the real-world landscape, receiving sensor data and location data from the sensors; and projecting the sensor data onto the map at locations indicated by the location data using the projectors corresponding to the sensors that the sensor data is received from.