SLAM Elevation Estimation Using Barometer and GPS Fusion
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Solution Overview
Problem
Conventional GPS-based elevation estimates for computing devices are not sufficiently accurate, often resulting in horizontal or vertical errors of up to ±15 meters, which is inadequate for applications like determining the floor level in multi-story buildings.
Innovation Solution
A system that utilizes simultaneous localization and mapping (SLAM) optimization by combining barometer data and GPS elevational data to improve the accuracy of elevation estimates, where GPS data provides noisy absolute constraints and barometer readings offer locally consistent relative constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS signals are used to estimate elevation, then absolute location information is obtained, but measurement precision deteriorates with errors of ±15 meters
Solution Approach 1:
The patent combines GPS elevation data (absolute but noisy) with barometer data (relative but consistent) through a unified optimization framework. This merging allows the system to leverage the strengths of both sensors: GPS provides absolute reference points while barometer provides continuous relative measurements, achieving both accuracy and reliability simultaneously.
Solution Approach 2:
The patent introduces an intermediary optimization process that mediates between GPS and barometer measurements. This intermediary layer reconciles the noisy absolute GPS data with the consistent relative barometer data, producing a final elevation estimate that is both accurate and reliable.
2Measurement precision
If barometer data alone is used for elevation estimation, then measurement precision improves with local consistency, but adaptability deteriorates due to lack of absolute reference
Solution Approach 1:
The patent merges barometer's locally consistent relative measurements with GPS's absolute elevation references. This combination allows the system to maintain the precision advantages of barometer while gaining the adaptability and absolute reference capability of GPS, resolving the contradiction between local consistency and absolute positioning.
Solution Approach 2:
The patent uses GPS elevation measurements as feedback to correct and calibrate barometer readings. This feedback mechanism allows the system to maintain absolute reference accuracy while preserving the continuous local consistency of barometer measurements, enabling both precision and adaptability.
3Measurement precision
If GPS data is used for horizontal localization, then absolute position is obtained, but measurement precision deteriorates in areas with physical barriers
Solution Approach 1:
The patent combines GPS horizontal localization (absolute but unreliable in barriers) with SLAM-based localization (relative but robust). This merging allows the system to use GPS when available for absolute positioning while falling back on SLAM techniques when GPS signals are blocked, maintaining both precision and reliability across different environments.
Solution Approach 2:
The patent introduces SLAM as an intermediary localization method that mediates between GPS availability and positioning needs. When GPS is blocked by physical barriers, SLAM provides a reliable alternative that maintains localization accuracy without depending on satellite signals.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the accuracy of elevation estimates, allowing for the construction of reliable elevation maps, floor identification, and terrain estimation, while also calibrating barometers and determining pressure at sea level.
Implementation Method 1
barometer readings offer locally consistent relative constraints
Implementation Method 2
GPS data provides noisy absolute constraints
Data Source
AI summary
A system includes one or more processors, and data storage configured to store instructions that, when executed by the one or more processors, cause the system to perform functions. In one example, the functions include receiving logs of data, wherein respective data in the received logs of data are collected by one or more sensors of a device over one or more locations and over a time period. In the present example, the functions also include determining location estimates of the device by performing a simultaneous localization and mapping (SLAM) optimization of the location estimates using barometer data and GPS elevational data available in the logs of data, wherein the location estimates indicate elevational locations of the device over the time period.