Indoor Navigation Maps Correcting Sensor Drift via Structural Feature Mapping
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Solution Overview
Problem
Mobile device sensors, such as those in cellular phones, suffer from errors like inertial drift and magnetic interference, leading to poor location accuracy indoors due to uncorrected data generation.
Innovation Solution
The creation and use of navigation maps that correct indoor location and heading accuracy by detecting and sharing structural features through inertial tracking, Wi-Fi, magnetic, and acoustic signals, allowing for improved location and routing services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensors are used for indoor location tracking, then location services can be provided, but location accuracy degrades over time due to inertial drift and magnetic interference
Solution Approach 1:
The system continuously compares sensor-derived location data with known structural features (walls, doors, floors) to detect drift and correct position estimates. This feedback loop maintains location accuracy over extended periods by constantly referencing the physical environment rather than relying solely on accumulating sensor data.
Solution Approach 2:
Structural features of the environment serve as intermediary reference points between the sensor system and the true location. By detecting features like walls and doors through sensors and comparing them against a map, the system uses these intermediaries to correct drift without requiring direct GPS-like positioning.
2Measurement precision
If multiple sensors and signals are used to improve location accuracy, then navigation precision increases, but system complexity increases
Solution Approach 1:
The system uses a single multi-functional sensor suite that performs multiple detection tasks. The same sensors detect both structural features for location correction and magnetic anomalies for feature identification, eliminating the need for separate specialized sensors and reducing overall system complexity while maintaining high precision.
Solution Approach 2:
The patent combines data from multiple sensor types (accelerometers, gyroscopes, magnetometers, Wi-Fi, acoustic sensors) into a unified processing framework. By merging these data streams and processing them together through a single algorithmic system, the patent achieves high measurement precision without proportionally increasing device complexity.
3Reliability
If structural features are detected and shared through crowdsourcing, then location corrections improve, but data processing and coordination complexity increases
Solution Approach 1:
Instead of sharing raw sensor data which would be complex and voluminous, the system creates simplified copies or representations of structural features (walls, doors, floors) and shares only these extracted geometric elements. This copying approach maintains location correction accuracy while dramatically reducing data coordination complexity.
Solution Approach 2:
The system extracts only the essential structural feature information (position, orientation, type of architectural elements) from complex sensor data streams and shares only these extracted features through the crowdsourcing network. This extraction process filters out unnecessary data complexity while preserving the information needed for accurate location corrections.
Data Source
AI summary
A location and mapping service is described that creates a global database of indoor navigation maps through crowd-sourcing and data fusion technologies. The navigation maps consist of a database of geo-referenced, uniquely described features in the multi-dimensional sensor space (e.g., including structural, RF, magnetic, image, acoustic, or other data) that are collected automatically as a tracked mobile device is moved through a building (e.g. a person with a mobile phone or a robot). The feature information can be used to create building models as one or more tracked devices traverse a building.