Signal-to-SLAM Map Alignment for Indoor Positioning Accuracy
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Solution Overview
Problem
Current indoor navigation methods lack effective solutions for accurately determining position and mapping in GPS-denied environments, such as indoor spaces, where satellite-based systems are ineffective due to attenuation by structures.
Innovation Solution
A mobile mapping system that uses a combination of sensors like inertial measurement units (IMUs), cameras, and 3D laser scanners to generate 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 GPS systems are used for outdoor location, then position determination is accurate in open environments, but position determination becomes poor or non-existent in indoor spaces due to signal attenuation by walls and structures
Solution Approach 1:
The patent introduces an intermediary system consisting of infrastructure-based transmitters and receivers, as well as visual features, that act as mediators between the satellite GPS system and indoor environments. These intermediaries enable position determination in indoor spaces by providing alternative signal paths and reference points that work within the constraints of building structures.
Solution Approach 2:
The patent develops multi-functional positioning systems that can operate across both outdoor and indoor environments. By combining infrastructure-based systems with visual SLAM techniques, the system achieves universal applicability across different environmental contexts, transitioning from GPS-dependent outdoor positioning to infrastructure or visual-dependent indoor positioning.
2Measurement precision
If infrastructure-based systems or active beacons are deployed for indoor navigation, then position determination works indoors, but device complexity and system cost increase
Solution Approach 1:
The patent uses visual copying by capturing images of the environment and creating digital representations through SLAM. These visual copies serve as maps that can be processed to determine position without requiring physical infrastructure installation. The system copies visual features from the real world and uses them as reference points for navigation.
Solution Approach 2:
The visual SLAM system enables devices to self-determine their position by autonomously detecting and tracking visual features in the environment. The system serves itself by using its own camera and processor to create maps and calculate position, eliminating the need for external infrastructure deployment.
3Device complexity
If visual SLAM techniques are used for indoor positioning, then no infrastructure is required, but measurement precision deteriorates in dynamic environments with moving objects
Solution Approach 1:
The patent implements dynamic adaptation in the visual SLAM system by continuously updating the environmental model and adjusting feature tracking based on detected changes. The system dynamically distinguishes between static environmental features and moving objects, maintaining accurate positioning by focusing on stable visual landmarks while filtering out dynamic elements that would otherwise cause errors.
4Reliability
If multiple sensor types are combined for robust positioning, then reliability improves in various conditions, but device complexity and energy consumption increase
Solution Approach 1:
The patent dynamically adjusts sensor activation and processing intensity based on environmental conditions and motion characteristics. The system changes operational parameters such as camera frame rate, processor utilization, and sensor fusion weightings to match the demands of the current situation, reducing energy consumption during stable periods while maintaining reliability when needed.
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.