Constraint-Based Sensor Map Data Fusion for Dead Reckoning Error Correction
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Solution Overview
Problem
Dead reckoning-based tracking systems suffer from cumulative error over long periods, and existing aiding sensors face challenges in indoor environments and non-Gaussian noise, limiting their accuracy and reliability for pedestrian tracking.
Innovation Solution
The implementation of a constraint-based convex optimization method for sensor and map data fusion, which uses convex SLAM algorithms to enforce distance and angle constraints on the tracking path, allowing for robust navigation solutions without assumptions on error distributions and maintaining the entire trajectory history for correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of moving object
If dead reckoning process is used for tracking, then continuous position estimation is achieved, but cumulative error increases over long periods
Solution Approach 1:
The patent implements feedback by using detected map features and range measurements to provide corrections to the dead reckoning trajectory through SLAM algorithms. The system continuously compares estimated position with observed features and adjusts the trajectory to reduce cumulative error, enabling accurate long-duration tracking.
Solution Approach 2:
The patent combines multiple data sources including dead reckoning sensors, map features, and range measurements into a unified SLAM framework. This merging of information sources allows the system to compensate for dead reckoning drift while maintaining continuous position estimation over extended periods.
2Reliability
If SLAM algorithms with Bayes filter are used for map and tracking fusion, then corrections for dead reckoning drift are provided, but computational complexity increases
Solution Approach 1:
The patent changes the mathematical parameters and assumptions in the SLAM formulation to enable more efficient computation. By modifying the error distribution assumptions and optimization parameters, the system achieves reliable tracking with reduced computational complexity compared to traditional Bayes filter approaches.
3Measurement precision
If aiding sensors are used to compensate for dead reckoning errors, then position accuracy is improved, but system complexity and indoor environment limitations increase
Solution Approach 1:
The patent creates a universal SLAM framework that can process multiple types of sensor data and map features through a unified algorithmic approach. This multi-functional system handles various sensor inputs and environmental conditions without requiring separate specialized processing paths, reducing overall system complexity while maintaining accuracy.
Data Source
AI summary
Disclosed herein are methods and systems for fusion of sensor and map data using constraint based optimization. In an embodiment, a computer-implemented method may include obtaining tracking data for a tracked subject, the tracking data including data from a dead reckoning sensor; obtaining constraint data for the tracked subject; and using a convex optimization method based on the tracking data and the constraint data to obtain a navigation solution. The navigation solution may be a path and the method may further include propagating the constraint data by a motion model to produce error bounds that continue to constrain the path over time. The propagation of the constraint data may be limited by other sensor data and/or map structural data.