Fused Compositional Mapping for Real-Time Object Identification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Detection of compositional information in an environment lacks sufficient detail to identify specific objects or features, and integrating contextual information can enhance the efficiency of detection devices.
Innovation Solution
A compositional visualization system that fuses contextual information with compositional measurements using a sensor, particle generator, and detector, transforming and merging data to generate a merged digital representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If compositional information is detected using active particle generation, then compositional characteristics can be obtained, but the information lacks sufficient detail to identify specific objects or features
Solution Approach 1:
The patent combines compositional information from particle interaction detectors with contextual information from environmental sensors (cameras, LIDAR, microphones) to create a unified data representation. This merging allows the system to maintain detailed compositional analysis while simultaneously capturing object identification and environmental context, resolving the information loss problem.
Solution Approach 2:
The system employs a multi-functional architecture where a single integrated platform performs both compositional analysis (through particle generation and detection) and environmental context acquisition (through multiple sensor types). This universal system eliminates the need for separate dedicated devices, allowing simultaneous collection of both compositional and contextual data streams.
2Productivity
If additional information regarding compositional characteristics and environmental objects is obtained, then detection efficiency is improved, but device complexity increases
Solution Approach 1:
The patent implements a universal detection platform that integrates particle generation, particle detection, and environmental sensing capabilities into a single system. This multi-functional architecture improves detection efficiency by simultaneously collecting compositional and contextual data while managing complexity through unified hardware and software integration rather than multiple separate devices.
Solution Approach 2:
The system incorporates autonomous data fusion capabilities where the integrated platform automatically correlates compositional data with contextual information without requiring external coordination. The unified system self-manages the complexity of processing multiple data streams, reducing operational burden and improving efficiency.
3Speed
If compositional measurements are taken without contextual information, then detection speed is maintained, but the ability to identify what objects specific characteristics correspond to is lost
Solution Approach 1:
The patent merges real-time compositional measurements with simultaneous contextual data acquisition from environmental sensors. By combining these data streams in unified processing, the system maintains detection speed while preventing information loss about object identification, as both types of data are collected and correlated concurrently rather than sequentially.
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 targeted identification of objects in an environment, providing compositional information in a remote and non-destructive manner, and updates information displays in real-time, combining different data types into a cohesive digital representation.
Implementation Method 1
a detector to receive a second stream comprising one or more detectable products. The second stream of particles is generated by interaction of the first stream with the environment
Data Source
AI summary
A compositional visualization system comprises a sensor to collect contextual information, a particle generator to generate a first stream of one or more types of particles, and a detector to receive a second stream of one or more detectable products. The second stream is generated by interaction of the first stream with the environment. The system further comprises computer-executable instructions to cause the system to transform the received second stream into compositional data, and merge the compositional data with the contextual information to generate a merged digital representation. The merged digital representation can be displayed at one or more devices and can also be used directly to drive autonomous robotic systems.