3D City Models and Shadow Mapping for Urban Altitude Fixes
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Solution Overview
Problem
In urban environments, GNSS signals are often blocked by tall structures, leading to poor position accuracy and availability, especially in altitude and across-street directions, as conventional shadow matching solutions rely on 2D city models and cannot distinguish between street-level and elevated positions.
Innovation Solution
The use of 3D city maps and shadow mapping to improve altitude fixes by predicting satellite visibility and matching it with real observations, incorporating altitude estimates from barometer data and 3D building models to refine position solutions, and calibrating barometer sensors for more accurate indoor positioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 2D city models are used for shadow matching, then the system complexity is low, but the position accuracy and altitude differentiation capability are insufficient
Solution Approach 1:
The patent transitions from 2D city models to 3D city models for shadow matching. This dimensional upgrade enables the system to differentiate between street-level and elevated positions by incorporating altitude information into the shadow matching process, thereby resolving the limitation of conventional 2D models that cannot distinguish vertical positions.
Solution Approach 2:
The patent changes the modeling parameter from two-dimensional coordinates to three-dimensional coordinates, adding the altitude dimension. This parameter change allows the shadow matching algorithm to evaluate satellite visibility at different elevations, improving position accuracy particularly in the vertical direction.
2Reliability
If conventional shadow matching is used, then the computational load is low, but the solution availability in urban canyons is poor
Solution Approach 1:
The patent performs preliminary computation of satellite visibility predictions based on 3D city models before actual position fixing. By pre-calculating which satellites should be visible from different locations and elevations, the system reduces real-time computational load while improving reliability in urban canyon environments where signal blockage patterns are complex.
Solution Approach 2:
The patent introduces barometer-derived altitude estimates as an intermediary to constrain the search space for position solutions. This intermediary information helps filter unrealistic candidate positions and improves solution availability in urban canyons by providing an additional constraint that reduces the impact of signal blockage.
3Measurement precision
If barometer data is incorporated for altitude estimation, then the altitude accuracy improves, but the sensor calibration complexity increases
Solution Approach 1:
The patent uses feedback from 3D shadow matching results to calibrate barometer sensors. The system compares barometer-derived altitude estimates with altitudes inferred from satellite visibility patterns in the 3D model, and uses this feedback to adjust and calibrate the barometer, improving altitude accuracy while managing calibration complexity through an automated feedback loop.
Solution Approach 2:
The system performs self-calibration of the barometer sensor using the 3D shadow matching framework. By leveraging the geometric constraints and satellite visibility predictions from the 3D city model, the system automatically calibrates the barometer without requiring external reference equipment or manual intervention, thereby improving altitude accuracy while keeping the system self-sufficient.
Data Source
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AI summary
The disclosed embodiments use 3D city models and shadow mapping to improve altitude fixes in urban environments. In an embodiment, a method comprises: generating a set of three-dimensional (3D) candidate positions in a geographic area of interest; predicting global navigation satellite system (GNSS) signal visibility at selected ones of the 3D candidate positions; receiving GNSS signals at a current location of the mobile device; determining observed satellite visibility based on the received GNSS signals; comparing the predicted satellite visibility with the observed satellite visibility; determining a position fix based on a result of the comparing; determining an indoor environment where the mobile device is located based at least on an altitude component of the position fix; obtaining structural data for the identified indoor environment; and determining a floor lower bound for the current location of the mobile device based on the altitude component and the structural data.