Nested Indoor Navigation Map for Centimeter Localization
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Solution Overview
Problem
Conventional indoor positioning systems fail to provide accurate and precise navigation for users with disabilities due to limitations in precision, hardware dependency, and internet connectivity, especially in indoor environments where GPS is ineffective.
Innovation Solution
A processor-implemented method and system that uses machine learning and augmented reality to create a two-dimensional digital map of indoor environments, allowing for precise user localization and dynamic path planning, avoiding obstacles and adapting to user constraints, without requiring additional hardware or internet access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional indoor positioning systems use Bluetooth beacons, RFID tags, Wi-Fi signature, or GPS with GIS, then navigation assistance can be provided, but localization accuracy and real-time precision are insufficient for users with disabilities
Solution Approach 1:
The system segments the indoor environment into a hierarchical nested structure with building-level maps containing floor-level maps containing room-level maps. This segmentation enables precise localization at the room level while maintaining overall system reliability through multi-level context, directly addressing the insufficiency of traditional single-level positioning systems for users with disabilities.
Solution Approach 2:
The patent introduces a hierarchical dimension to traditional indoor positioning by creating nested maps across multiple levels (building→floor→room). This dimensional transformation enables centimeter-level precision within rooms while maintaining reliable navigation across entire facilities, resolving the contradiction between localization accuracy and navigation reliability that plagues conventional systems.
2Adaptability or versatility
If GPS is used for outdoor navigation, then navigation assistance can be provided, but it fails to show accurate navigation in indoor environments
Solution Approach 1:
The system creates a universal nested map structure that functions across multiple environments and scales. The same hierarchical mapping approach (building→floor→room) works whether the user is navigating a small office or a large hospital complex, providing both environmental adaptability and centimeter-level precision through the consistent application of the nested map framework.
Solution Approach 2:
The patent implements nested maps where building-level maps contain multiple floor-level maps, and each floor contains multiple room-level maps. This nesting structure enables the system to adapt to any indoor environment size while maintaining high precision navigation at the room level, effectively replacing GPS functionality indoors with a scalable hierarchical alternative.
3Ease of operation
If turn by turn directional assistance is provided using traditional systems, then navigation can be assisted, but accurate real-time localization is not achieved
Solution Approach 1:
The system performs preliminary actions by pre-processing the indoor environment into a detailed nested map structure with rooms, corridors, and landmarks identified before navigation begins. This preliminary mapping enables real-time turn-by-turn directional assistance with centimeter-level precision, as the system already has a prepared hierarchical framework to match against sensor data during navigation.
Solution Approach 2:
The patent replaces traditional mechanical positioning systems (Bluetooth beacons, RFID tags, Wi-Fi signatures) with a computational approach using nested maps and sensor fusion. This substitution achieves both ease of operation for turn-by-turn guidance and high spatial location precision by using algorithmic matching between sensor data and the hierarchical map structure rather than relying on physical positioning infrastructure.
4Adaptability or versatility
If conventional positioning systems are used, then navigation can be provided, but additional hardware and internet connectivity are required
Solution Approach 1:
The system enables self-service navigation by using the smartphone's existing sensors (accelerometer, gyroscope, magnetometer, camera) to create and navigate within nested maps. No additional positioning hardware or internet connectivity is required, as the system uses onboard sensors to track movement and match against the pre-loaded hierarchical map structure, achieving both system independence and reduced device complexity.
Solution Approach 2:
The patent discards dependency on external positioning infrastructure (Bluetooth beacons, RFID tags, Wi-Fi signatures, internet connectivity) and recovers positioning capability using only the smartphone's built-in sensors. This approach eliminates hardware dependency while maintaining navigation functionality, as the system recovers location information through sensor fusion and matching against the nested map structure stored locally on the device.
Data Source
AI summary
This disclosure relates to systems and methods for performing inclusive indoor navigation. State of the art systems and methods require extra hardware and fail to provide accurate localization and navigation with less precision. The method of the present disclosure obtains a nested environment data of a facility and estimate current spatial location of a user in the nested environment using surrounding recognition machine learning model. An optimal path categorized as convenient path, shortest path and multi-destination path from the current spatial location to a destination is determined. The current spatial location of the user is tracked on the optimal path using an augmented reality technique when navigation starts. The optimal path is dynamically updated based on feedback obtained from one or more user interaction modalities. The present disclosure provides user navigation with last meter precision, and no hardware and internet dependency.