Hybrid Sensor Fusion Navigation for Dynamic Indoor Positioning
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Solution Overview
Problem
Existing navigation systems for visually impaired individuals are inadequate in dynamically changing indoor environments, lacking accuracy and safety in guiding them to target assets.
Innovation Solution
A network entity and wireless device system that utilizes image data from cameras, haptic and audio outputs, and RF communication to provide navigation commands and instructions, enabling safe and accurate navigation to target assets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional navigation systems are used for visually impaired individuals, then the system complexity is low, but the navigation accuracy and safety are insufficient in dynamic indoor environments
Solution Approach 1:
The patent combines multiple sensor systems (cameras, LIDAR, RFID readers, inertial measurement units) into a unified navigation system. The network entity integrates data from these diverse sensors to create a comprehensive navigation solution that overcomes the limitations of individual sensors, achieving high accuracy in dynamic indoor environments through sensor fusion.
Solution Approach 2:
The navigation system is designed to perform multiple functions: obstacle detection, positioning, route planning, and real-time navigation guidance. The hybrid sensor fusion architecture enables a single system to handle various navigation tasks simultaneously, providing universal functionality for visually impaired individuals in diverse indoor scenarios.
2Reliability
If hybrid sensor fusion with multiple cameras and sensors is implemented, then navigation reliability improves, but device complexity increases
Solution Approach 1:
The network entity serves as an intermediary that centralizes the complex processing of multiple sensor inputs. Rather than requiring the wireless device to handle all sensor fusion computations locally, the network entity mediates between the distributed sensors and the navigation output, managing the complexity centrally while maintaining reliability through coordinated multi-sensor operation.
Solution Approach 2:
The system performs preliminary processing and calibration of sensor data before full navigation operations begin. The network entity pre-processes sensor inputs, establishes baseline measurements, and prepares fusion algorithms in advance, reducing the real-time computational burden and enhancing reliability by ensuring all sensor systems are properly synchronized and calibrated before critical navigation decisions.
3Adaptability or versatility
If real-time image data from cameras is processed for route planning, then navigation adaptability to dynamic environments improves, but energy consumption increases
Solution Approach 1:
The system processes image data at selective intervals and only when necessary for route adjustments, rather than continuously analyzing all sensor inputs. The network entity determines when full image processing is needed versus when simpler sensor fusion suffices, reducing energy consumption while maintaining adaptability to significant environmental changes through event-triggered processing.
4Measurement precision
If multiple navigation sensors and processing systems are integrated, then information accuracy improves, but loss of time in processing increases
Solution Approach 1:
The system performs preliminary calibration and synchronization of multiple sensors during initialization and idle periods. By pre-processing sensor data, establishing coordinate systems, and synchronizing clocks in advance, the system reduces real-time processing requirements, achieving high positioning accuracy without excessive processing delays during critical navigation moments.
Data Source
AI summary
Disclosed are techniques for enhanced navigation; for example, of visually impaired persons, automated guided vehicles, etc. In an aspect, a network entity such as a server may obtain information indicative of a navigation environment and information indicative of an estimated traversal distance for a plurality of potential routes from an initial position of a wireless device to one or more locations proximate one or more target assets by obtaining image data from one or more cameras positioned to monitor one or more segments of the plurality of potential routes. The network entity may select a first route from the initial position of the wireless device to at least a first location of the one or more locations.