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Zonotopic Linear Parameter Varying SLAM Applied to Autonomous Vehicles.
Marc Facerias1, Vicenç Puig2, Eugenio Alcala2
1Autonomous Systems, Department of Electrical and Electronic Engineering, University of Manchester, Sackville Street Building, Manchester M1 3BB, UK.
Sensors (Basel, Switzerland)
|May 28, 2022
Summary
This study introduces a new autonomous driving localization method using polytopic Linear Parameter Varying (LPV) systems and zonotopes for precise vehicle and landmark positioning. The approach guarantees state estimation without noise assumptions, enhancing robot navigation.
Area of Science:
- Robotics
- Control Systems
- Computer Vision
Background:
- Accurate vehicle localization is critical for autonomous driving.
- Existing methods often rely on specific noise assumptions.
- Set-based Kalman filtering offers robust state estimation.
Purpose of the Study:
- To develop a precise and robust localization approach for autonomous vehicles.
- To leverage polytopic Linear Parameter Varying (LPV) systems and zonotopes for state estimation.
- To provide guaranteed localization without strict noise assumptions.
Main Methods:
- Utilized polytopic Linear Parameter Varying (LPV) systems to model vehicle dynamics.
- Applied set-based methodologies with Kalman filters, specifically zonotopes, for state estimation.
- Developed an LPV-model predictive controller and a Zonotopic Kalman filter for localization and navigation.
- Validated the control and estimation scheme in simulation using the Robotic Operating System (ROS).
Main Results:
- Achieved precise localization of both the vehicle and surrounding landmarks.
- Provided guaranteed state estimation independent of noise distribution, relying only on noise bounds.
- Demonstrated the effectiveness of the LPV-based control and zonotopic filtering approach.
- Successfully validated the integrated system in a simulated ROS environment.
Conclusions:
- The proposed approach offers a robust and guaranteed method for autonomous vehicle localization.
- LPV systems and zonotopic Kalman filters provide a powerful combination for navigation tasks.
- The validated simulation results confirm the practical applicability of the developed framework.
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