Position Estimation in Multiple Coordinate Systems
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Solution Overview
Problem
Existing systems for simultaneous position estimation in multiple coordinate systems are computationally intensive, requiring powerful and expensive computers, and often involve redundant calculations.
Innovation Solution
A procedure for position estimation that involves receiving sensor data for a relative local coordinate system, performing a position estimate, and using the results to inform an absolute global position estimate, while fusing data from a global navigation satellite system and determining correlations between conditions to generate a state clone.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If simultaneous position estimation is performed in multiple coordinate systems using independent complete calculations, then measurement precision and reliability are improved, but device complexity and computing power requirements increase significantly
Solution Approach 1:
The position estimation system is segmented into multiple coordinate system estimators (e.g., local coordinate system estimator and global coordinate system estimator) that operate independently but share sensor data. Each estimator performs position estimation for its specific coordinate system requirements, avoiding the need for a single complex estimator to handle all coordinate systems simultaneously. This segmentation reduces device complexity while maintaining precision for each coordinate system.
Solution Approach 2:
The sensor data processing system is designed with multi-functionality to serve multiple coordinate system estimators. The same sensor data input is universally processed by different estimators (local, global, and intermediate coordinate systems) according to their specific requirements. This universal data processing approach eliminates redundant data acquisition and reduces computing power requirements while maintaining measurement precision.
2Measurement precision
If complete position estimation calculations are performed for each coordinate system independently, then measurement precision is improved, but productivity decreases due to runtime intensity
Solution Approach 1:
The system performs preliminary position estimation calculations in intermediate coordinate systems using sensor data before final coordinate system transformations are required. By pre-computing position estimates in multiple coordinate systems and storing them for later use, the system avoids performing complete calculations repeatedly, thereby improving computational efficiency while maintaining precision.
Solution Approach 2:
The position estimation calculations for different coordinate systems are merged into a unified processing framework where sensor data is processed once and results are shared across multiple estimators. This merging eliminates redundant calculations and improves productivity by reducing the total computational load, while each estimator still receives the precision it needs for its specific application.
3Device complexity
If simplifications are introduced to reduce computing power requirements, then device complexity is reduced, but measurement precision and reliability deteriorate
Solution Approach 1:
Different coordinate system estimators are configured with local quality appropriate to their specific application requirements. For example, the local coordinate system estimator may use simplified algorithms suitable for short-term stability, while the global coordinate system estimator uses more precise algorithms for long-term drift compensation. This local optimization allows each estimator to achieve the necessary precision for its specific purpose without requiring all estimators to use computationally intensive methods.
Solution Approach 2:
The system dynamically adjusts estimation parameters such as covariance estimation depth and smoothing constraints based on the specific coordinate system and application requirements. By changing parameters like the level of covariance calculation detail or the strictness of smoothness constraints, the system can reduce computing power requirements for less critical estimators while maintaining high precision for critical estimators, thus resolving the contradiction between device complexity and measurement precision.
Data Source
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AI summary
The present invention relates to a method, a computer program with instructions, and a device for position estimation, in particular for simultaneous position estimation in at least two coordinate systems. In a first step (S1), sensor data for position estimation in a first coordinate system are received. Using the sensor data, a position estimation is then performed in the first coordinate system (S2). The position information resulting from the position estimation in the first coordinate system is subsequently provided for position estimation in a second coordinate system (S3). The provided position information is then fused into a position estimation in the second coordinate system (S4). Finally, the position estimations are output to at least one external application (S5).