Mobile Mapping Platform for GPS-Poor Spatial Reference
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Solution Overview
Problem
Current methods for creating globally referenced maps in GPS-poor spaces, such as indoors or areas with inadequate satellite reception, result in significant errors and are time-consuming, as they rely on manual measurements and lack seamless navigation between indoor and outdoor spaces.
Innovation Solution
A method using a mobile platform with mapping sensors and processors to generate maps in a universal uniform spatial reference frame, allowing for the transformation of positions into GPS coordinates, enabling accurate and automated mapping of spaces with inadequate GPS reception.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual measurement methods are used to create maps in GPS-poor spaces, then the maps can be created without GPS signals, but the process is time-consuming and cumulative errors result in inaccurate positioning
Solution Approach 1:
The patent replaces manual mechanical measurement systems (tape measures, hand-held laser range finders) with an automated mobile mapping platform that integrates GPS receivers, inertial measurement units (IMU), and laser range finders. This substitution eliminates human error in measurement and positioning while dramatically reducing the time required to create accurate maps of GPS-poor spaces.
Solution Approach 2:
The system dynamically switches between different positioning parameters and methods based on GPS signal availability. When GPS signals are weak or unavailable, the system transitions to using IMU data, odometry from wheel encoders, and relative positioning from laser range finders. This parameter changes approach maintains positioning accuracy across varying environmental conditions without manual intervention.
2Extent of automation
If visual pattern-matching techniques are used for mapping, then the process can be automated, but inaccuracies result due to imprecise matching
Solution Approach 1:
The patent introduces multiple intermediary systems between the mobile platform and the final map output. These include IMU sensors that provide intermediate positioning data, laser range finders that create intermediate point clouds of the environment, and integration algorithms that fuse these intermediate results into accurate maps. This multi-layer intermediary approach maintains high automation while achieving precision superior to simple visual pattern-matching.
3Adaptability or versatility
If GPS coordinates are used for mapping, then seamless navigation between indoor and outdoor spaces is enabled, but GPS signals are not available indoors or in GPS-poor areas
Solution Approach 1:
The mapping system dynamically adapts its positioning strategy based on real-time GPS signal conditions. The mobile platform continuously monitors GPS signal strength and automatically transitions between absolute GPS positioning and relative positioning modes using IMU and odometry. This dynamic adaptation ensures seamless navigation capability across both GPS-rich outdoor environments and GPS-poor indoor areas without interruption or manual reconfiguration.
Solution Approach 2:
The mobile mapping platform is designed with multi-functional positioning capabilities that work across all environments. It integrates GPS receivers for outdoor positioning, IMU sensors for inertial navigation, wheel encoders for odometry, and laser range finders for relative mapping. This universal system performs all positioning functions through a single integrated platform, eliminating the need for separate systems for indoor and outdoor navigation.
4Productivity
If CAD drawings are used to represent building interiors, then maps can be created quickly, but they do not reflect actual building conditions and cannot be accurately related to exterior GIS coordinates
Solution Approach 1:
The mobile mapping platform performs self-service by automatically capturing and processing spatial data without requiring separate CAD drawing creation or manual coordinate transformation. The integrated system simultaneously records GPS coordinates, IMU orientation data, and laser range finder measurements, automatically generating accurate as-built maps that reflect actual building conditions and are precisely related to exterior GIS coordinates through real-time coordinate transformation algorithms.
Data Source
AI summary
A method for generating maps of a desired area that are referenced to a universal uniform spatial reference frame includes a mobile platform having a mapping processor and at least one mapping sensor is provided. The mapping sensor is applied to determine the location of features to be mapped relative to the mobile platform. The mobile platform is moved to a new location, and the mapping sensor is further applied to determine the location of features to be mapped relative to the mobile platform. These steps are repeated until the desired area is mapped. The at least one mapping sensor is also applied to locate at least one position on the map of the desired area on a universal uniform spatial reference frame. Positions on the map of the desired area are transformed into the universal uniform spatial reference frame.