Navigation System Using Location Context Identifiers for Indoor Outdoor Routing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing navigation systems face difficulties in providing seamless routing solutions for mobile stations that traverse both indoor and outdoor areas, particularly when multiple floors are involved, due to differences in map scales, routing algorithms, and location identification technologies.
Innovation Solution
The system employs location context identifiers (LCIs) to seamlessly transition between maps by identifying points of interest (POIs) with embedded LCI information, allowing for the retrieval of subsequent maps and continuous route determination across different areas, including indoor and outdoor transitions and multiple floors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single map is used for navigation, then the system is simple to operate, but it cannot provide seamless routing across indoor and outdoor areas with different scales and contexts
Solution Approach 1:
The navigation system divides the overall map into multiple context-specific map segments (indoor maps and outdoor maps). Each map segment represents a specific environment type with its own scale, detail level, and routing rules. This segmentation allows the system to handle complex multi-environment navigation while keeping each individual map segment manageable and optimized for its specific context.
Solution Approach 2:
The system implements a universal map management framework that can handle both indoor and outdoor maps through a common interface. The location context identifier serves as a universal key that works across different map types, allowing the same navigation algorithms and user interface to operate seamlessly whether the user is indoors or outdoors, thus providing multi-functionality without increasing operational complexity.
2Measurement precision
If multiple detailed maps are maintained for different areas, then navigation precision is improved, but memory requirements and data management complexity increase
Solution Approach 1:
The system applies local quality by providing different levels of map detail appropriate to each location context. Indoor maps contain detailed information about rooms, corridors, and specific points of interest, while outdoor maps provide broader contextual information about buildings, streets, and landmarks. This localized optimization ensures high precision where needed without uniformly increasing data volume across all maps.
Solution Approach 2:
The map data structure implements a nested organization where detailed indoor maps are nested within broader outdoor map contexts. When navigating outdoors, the system displays outdoor map data; when transitioning indoors, it seamlessly loads and displays the corresponding nested indoor map with higher detail. This nesting allows the system to maintain multiple detailed maps without proportionally increasing memory requirements, as maps are loaded and unloaded based on current location context.
3Ease of operation
If the system automatically switches between maps, then user convenience is improved, but the complexity of map transition management increases
Solution Approach 1:
The location context identifier acts as an intermediary that mediates between the user's physical location and the appropriate map representation. Instead of implementing complex logic to determine when and how to switch between indoor and outdoor maps, the system uses the LCI as a simple key that directly maps to the appropriate map segment. This intermediary simplifies the transition management logic while providing seamless automatic map switching based on the user's location context.
4Measurement precision
If comprehensive routing algorithms are implemented for all environments, then routing accuracy is improved, but computational requirements and processing time increase
Solution Approach 1:
The routing system dynamically adapts its algorithms and parameters based on the current location context. When operating in outdoor environments, the system uses routing algorithms optimized for larger scales and broader pathways. When transitioning to indoor environments, it automatically switches to algorithms optimized for smaller scales, detailed corridors, and specific destination access. This dynamic adaptation ensures high routing accuracy for each environment type while minimizing computational requirements by not running all algorithms in all contexts.
Data Source
AI summary
Examples disclosed herein may relate to identifying up to a specified amount of points of interest nearest to an estimated position of a mobile station on a first map, selecting a second map identified by a first location context identifier associated with a first point of interest of the identified points of interest, and determining a route from the estimated position of the mobile station to a destination point using the first map and the second map.