Evidence Grid Sensor Data Alignment for Navigation Error Correction
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Solution Overview
Problem
Existing systems for creating three-dimensional maps using ranging sensors like radar or lidar in vehicles face limitations due to restricted field of view, obscurants, and navigation errors, resulting in low-resolution maps, while high-resolution a priori data from different platforms cannot be directly used due to temporal changes and navigation inaccuracies.
Innovation Solution
The integration of a priori data is achieved by recasting it as virtual sensor output and aligning it with real-time sensor data using an optimization algorithm within an evidence grid, correcting for navigation errors and ensuring the data is up-to-date.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution a priori data is used to improve map resolution, then map resolution is improved, but the data becomes outdated due to temporal changes
Solution Approach 1:
The patent combines a priori data with real-time sensor data through an evidence grid framework. The a priori data provides high-resolution terrain information while real-time sensor data captures current conditions, merging both sources to achieve high resolution without sacrificing data currency.
Solution Approach 2:
The patent performs preliminary alignment and registration of a priori data with real-time sensor data using optimization algorithms. This preliminary action ensures that the high-resolution a priori data is properly positioned and oriented before being integrated, resolving navigation errors and ensuring spatial correspondence.
2Reliability
If real-time sensor data is used to ensure current information, then data currency is improved, but map resolution deteriorates due to limited field of view and obscurants
Solution Approach 1:
The evidence grid framework merges real-time sensor data with a priori data, allowing the system to maintain data currency from real-time sensors while achieving high resolution through the complementary a priori information.
3Device complexity
If a priori data is displayed without correction to simplify the system, then device complexity is reduced, but navigation errors cause misalignment with current position
Solution Approach 1:
The patent employs optimization algorithms that use feedback from the comparison between a priori data and real-time sensor data to iteratively adjust and correct navigation errors. This feedback mechanism automatically compensates for position and orientation errors without requiring complex manual correction systems.
Solution Approach 2:
The system performs self-correction by using the optimization algorithm to automatically align a priori data with real-time sensor data, eliminating the need for external intervention or complex manual alignment procedures.
Data Source
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AI summary
Systems and method for improving the presentation of sensed data (e.g., radar) by including a priori data. The a priori data is recast as if it were the output of a sensor. This allows the inclusion of the a priori data into an evidence grid that is combined with data from multiple types of sensors (26). Before the sensor data is combined into the evidence grid, the sensor data is aligned with the a priori data using an optimization algorithm. The optimization algorithm provides an optimum probability of a match between the sensor data and the a priori data by adjusting position or attitude associated with the sensor device. This removes any navigational errors associated with the sensor device data.