Vehicle Position Estimation Using Sensor Fusion and Reliability Filtering
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Solution Overview
Problem
Vehicle navigation systems face position estimation errors due to GNSS signal inaccuracies, which can range from 10 to 100 meters, and existing methods struggle to correct these errors effectively, especially in environments with obstacles or satellite signal blockages.
Innovation Solution
A position estimating apparatus that combines data from a main sensor, such as an IMU and GPS, with auxiliary sensors like cameras, using sensor fusion techniques like Kalman filtering and nonlinear filtering to accurately determine vehicle position, selectively incorporating auxiliary data based on its reliability to minimize errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GNSS signals are used for position estimation, then the system can provide global coverage and basic positioning functionality, but the position estimation error ranges from 10 to 100 meters
Solution Approach 1:
The patent combines multiple sensing systems (GNSS receiver, IMU, camera, barometer, magnetometer) into an integrated sensor fusion system. The processor fuses data from all these sensors using algorithms like Kalman filtering to produce a more accurate and reliable position estimate than any single sensor could provide alone, resolving the contradiction between GNSS coverage and accuracy.
Solution Approach 2:
The patent introduces auxiliary sensors (camera, barometer, magnetometer) as intermediary systems that provide additional measurement data to compensate for GNSS signal weaknesses. These auxiliary sensors act as mediators that fill the accuracy gap when GNSS signals are obstructed or inaccurate, enabling reliable positioning in challenging environments.
2Measurement precision
If auxiliary sensors are selectively incorporated based on reliability, then the position estimation accuracy improves to less than 1 meter error, but the device complexity increases due to multiple sensors and fusion algorithms
Solution Approach 1:
The patent implements dynamic sensor selection where the processor selectively incorporates auxiliary sensing data based on real-time reliability assessments. The system adaptively adjusts which sensors are active and how their data is weighted, optimizing the balance between accuracy improvement and computational complexity rather than using all sensors at full capacity continuously.
Solution Approach 2:
The patent changes the reliability parameter threshold dynamically to control the incorporation of auxiliary sensor data. By adjusting reliability thresholds and fusion weights based on current operating conditions, the system optimizes accuracy while managing computational complexity, incorporating only when and where it provides genuine benefit.
Data Source
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AI summary
Disclosed is a position estimating method and apparatus that estimates a position based on main sensing data and secondarily determines the position based on the main sensing data and auxiliary sensing data when the auxiliary sensing data is found to be reliable.