Signal-to-SLAM Data Alignment for GPS-Free Indoor Localization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current navigation systems, such as GPS, are ineffective indoors due to signal attenuation, necessitating alternative methods for locating and mapping indoor environments without reliance on external location technologies.
Innovation Solution
A mobile mapping system that utilizes a combination of sensors like inertial measurement units (IMU), cameras, and 3D laser scanners to create real-time, GPS-independent maps and estimate position through simultaneous localization and mapping (SLAM) techniques, capable of operating in dynamic environments and handling sensor degradation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If satellite-based navigation systems (GPS) are used for outdoor location, then position accuracy is improved, but the system becomes ineffective indoors due to signal attenuation
Solution Approach 1:
The patent introduces an intermediary system consisting of infrastructure-based transmitters and receivers that mediate location determination indoors. When GPS signals are unavailable, the system switches to using local transmitters that emit signals detectable by receivers in the mobile device, enabling continuous location tracking across both indoor and outdoor environments without interruption
Solution Approach 2:
The patent creates a universal location determination system that can operate in multiple environments (indoor and outdoor) by integrating multiple positioning methods. The system selectively uses GPS when available and transitions to infrastructure-based or sensor-based methods when GPS is unavailable, providing consistent location services across diverse operational contexts
2Measurement precision
If infrastructure-based methods are used for indoor navigation, then position determination is improved, but device complexity increases
Solution Approach 1:
The patent implements self-service through sensor fusion where the mobile device uses its own onboard sensors (accelerometers, gyroscopes, magnetometers) to determine position when infrastructure-based methods are unavailable. The system autonomously switches between positioning methods and uses dead reckoning with sensor data to maintain location tracking without requiring external infrastructure
Solution Approach 2:
The patent applies dynamics by making the positioning system adaptive and flexible. The system dynamically selects the most appropriate positioning method based on availability and accuracy requirements, transitioning between GPS, infrastructure-based methods, and sensor-based dead reckoning to optimize performance while managing complexity
3Measurement precision
If sensor fusion is used for SLAM localization, then position tracking accuracy is improved, but computational requirements and processing time increase
Solution Approach 1:
The patent applies preliminary action by pre-processing sensor data and pre-computing transformation matrices and calibration parameters. The system performs preliminary alignment of sensor coordinate systems and pre-calculates rotation and translation matrices that are then applied in real-time during SLAM operations, reducing computational burden during actual position tracking
Solution Approach 2:
The patent segments the complex SLAM computation into distinct modules: sensor data acquisition, preprocessing and filtering, feature extraction, pose estimation, and map building. This segmentation allows each module to be optimized independently and enables parallel processing of different sensor streams and computational tasks, reducing overall processing time
Data Source
AI summary
A method includes retrieving a map of a 3D geometry of an environment the map including a plurality of non-spatial attribute values each corresponding to one of a plurality of non-spatial attributes and indicative of a plurality of non-spatial sensor readings acquired throughout the environment, receiving a plurality of sensor readings from a device within the environment wherein each of the sensor readings corresponds to at least one of the non-spatial attributes and matching the plurality of received sensor readings to at least one location in the map to produce a determined sensor location.