Multi-Sensor Fusion for Real-Time Geospatial Mapping
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Solution Overview
Problem
Current technologies face challenges in integrating diverse sensor data from multiple sources, such as cameras, LIDAR, and GPS, to provide real-time geospatial mapping and navigation for mobile robots, often requiring expensive high-end IMUs and GPS systems, and struggle with efficient data compression and analytics.
Innovation Solution
A computing system that integrates multi-sensor, multi-modal data in real-time using a software pipeline with modules for sensor data capture, synchronization, navigation, geospatial mapping, live analytics, and compression, enabling low-latency map updates and improved accuracy through domain-specific business logic and analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expensive high-end IMUs and GPS systems are used, then navigation accuracy is improved, but system cost increases
Solution Approach 1:
The patent combines multiple low-cost sensors (camera, LIDAR, GPS, IMU) into an integrated navigation system that processes data from all sources simultaneously. This fusion approach replaces the need for expensive high-end IMUs and GPS systems while maintaining navigation accuracy through collaborative sensor operation and data integration.
Solution Approach 2:
The system employs a multi-functional sensor suite where each sensor serves multiple purposes: the camera provides both visual navigation data and geospatial context, the LIDAR contributes to both 3D mapping and position verification, and the GPS/IMU combination supports both location tracking and orientation. This multi-functionality allows low-cost components to collectively replace expensive specialized equipment.
2Measurement precision
If real-time geospatial mapping is performed, then mapping accuracy is improved, but data processing time increases
Solution Approach 1:
The patent segments the geospatial mapping process into distinct functional modules: sensor data capture, synchronization, 3D mapping, georeferencing, and analytics. Each module processes specific aspects of the data independently, allowing parallel computation and reducing overall processing time while maintaining mapping accuracy through coordinated operation of all segments.
Solution Approach 2:
The system performs preliminary data synchronization and preprocessing before main mapping operations. Sensors are synchronized in advance, and raw data is pre-processed and organized into structured formats before being fed into the mapping algorithms. This preliminary action reduces the computational burden during real-time mapping, enabling faster processing without sacrificing accuracy.
3Measurement precision
If multi-sensor data integration is performed, then geospatial mapping accuracy is improved, but device complexity increases
Solution Approach 1:
The data integration system is segmented into specialized modules: a synchronization module that aligns timestamps from different sensors, a calibration module that establishes coordinate transformations, and a fusion module that combines processed data. This segmentation reduces integration complexity by breaking down the complex multi-sensor fusion task into manageable, independent functions that can be developed and maintained separately.
Solution Approach 2:
The patent introduces intermediary processing layers between raw sensor data and the final mapping output. These intermediaries include synchronized time stamps, calibrated coordinate systems, and pre-processed feature extractions that serve as standardized interfaces between different sensor types. These intermediaries simplify the integration process by providing uniform data formats and reducing direct complexity between heterogeneous sensors.
Data Source
AI summary
A multi-sensor, multi-modal data collection, analysis, recognition, and visualization platform can be embodied in a navigation capable vehicle. The platform provides an automated tool that can integrate multi-modal sensor data including two-dimensional image data, three-dimensional image data, and motion, location, or orientation data, and create a visual representation of the integrated sensor data, in a live operational environment. An illustrative platform architecture incorporates modular domain-specific business analytics “plug ins” to provide real-time annotation of the visual representation with domain-specific markups.