Vehicle Localization via Kalman Filter Odometer Calibration
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Solution Overview
Problem
Current vehicle localization methods face challenges in accuracy due to sensor-related errors and the lack of continuous calibration, especially when GNSS signals are unavailable, leading to potential localization errors in tunnels or areas with poor coverage.
Innovation Solution
A computer-implemented method using GNSS data and vehicle sensor data, employing a Kalman filter for real-time calibration of odometer parameters, which predicts and corrects distance and orientation variations, ensuring accurate localization even without continuous GNSS signal availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GNSS data is used for vehicle localization, then localization accuracy is improved, but the system becomes unreliable when GNSS signal is unavailable (e.g., in tunnels or areas with poor coverage)
Solution Approach 1:
The system performs preliminary calibration of the odometer parameters using GNSS data when available, storing the calibrated parameters for later use. This preliminary action ensures that when GNSS signal is lost, the system already has accurate calibration data to maintain localization reliability without continuous GNSS coverage.
Solution Approach 2:
The calibrated odometer parameters act as an intermediary between the GNSS data and the final localization output. When GNSS signal is unavailable, the intermediary calibrated parameters enable the system to continue providing accurate localization by bridging the gap between GNSS-based calibration and sensor-based operation.
2Adaptability or versatility
If sensor data is used for vehicle motion tracking, then the system can operate without GNSS signal, but sensor-related errors accumulate and reduce localization accuracy
Solution Approach 1:
The system uses feedback from GNSS data to continuously calibrate and correct the odometer parameters. This feedback mechanism allows the system to compensate for sensor-related errors by adjusting the calibration parameters based on actual GNSS measurements, thereby maintaining high localization accuracy even when operating without continuous GNSS signal.
Solution Approach 2:
The system dynamically changes the calibration parameters of the odometer based on available GNSS data. By updating these parameters in real-time or near-real-time, the system adapts to sensor drift and error accumulation, ensuring that the localization accuracy is maintained regardless of whether GNSS signal is currently available.
3Measurement precision
If continuous calibration using GNSS data is performed, then localization accuracy is maintained, but the system cannot function when GNSS signal is lost
Solution Approach 1:
The system performs calibration actions in advance when GNSS signal is available, storing the calibrated parameters for later use. This preliminary calibration ensures that the system is ready to operate autonomously with high accuracy when GNSS signal is lost, eliminating the need for continuous GNSS coverage during operation.
Solution Approach 2:
The system uses the calibrated odometer parameters to self-correct and maintain localization accuracy without requiring continuous external GNSS reference. The calibrated parameters enable the system to service its own localization needs independently, allowing it to function accurately in GNSS-denied areas by relying on its pre-calibrated sensor model.
Data Source
AI summary
The disclosure notably relates to a computer-implemented method for localization of a moving vehicle based on GNSS data and vehicle sensor data. The method comprises, in real-time, obtaining vehicle motion data stemming from at least one vehicle sensor. The method also comprises obtaining, while the GNSS signal is available, GNSS data of a positioning of the vehicle. The GNSS data includes a distance variation and an orientation variation. The method also comprises calibrating parameters of an odometer of the vehicle. The calibration is based on a data fusion that uses a Kalman filter. The Kalman filter determines a predicted distance variation and a predicted orientation variation of the vehicle based on a current calibration of the odometer parameters and on the motion data. The Kalman filter also compares the predicted distance variation and predicted orientation variation to the distance variation and the orientation variation of the GNSS data.