Kinematic/Dynamic SLAM for Autonomous Vehicles Using the Linear Parameter Varying Approach
Abstract
:1. Introduction
- A SLAM LPV KF scheme is proposed that does not require the linearization of the nonlinear vehicle model but instead embeds the nonlinearities in the varying parameters.
- The proposed SLAM approach considers both the kinematic and dynamic model of the vehicle, allowing it to operate at higher speeds compared to the pure kinematic schemes available in the literature.
- A design procedure based on the LMI framework allows the offline design of the LPV KF, reducing the online computational complexity.
- The LMI framework is rooted in the Lyapunov stability theory guaranteeing the quadratic stability of the LPV KF scheme.
2. Problem Statement
2.1. Vehicle Modelling
2.2. Map Modelling
- The sensor is not placed at the centre of gravity of the vehicle but at a fixed point of the vehicle frame described by the pose and the orientation . The new frame that can be defined from this point is called the sensor frame .
- To reduce the nonlinearity of the observation model, a transformation block of polar coordinates to Cartesian coordinates is installed at the sensor output:
2.3. Motion Model for the SLAM System
3. Proposed Solution
3.1. Dynamic State Estimation
3.2. Kinematic State Estimation and SLAM
3.3. State Estimation Scheme
4. Design Procedures
4.1. Online Approach
4.2. Offline Approach
5. Simulation Results
5.1. Simulation Set-Up
5.2. Simulation Scenarios
6. Conclusions
- The proposed approach will be applied and tested in the real vehicle in the context of current ongoing research project.
- The extension of the proposed observation approach to take into account 3D scene factors (such as ground unevenness and vehicle instability) will be explored.
- The inclusion of range sensors instead of state sensors will also be considered.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Dynamic Model Parameters of Tazzari Zero Vehicle | ||
---|---|---|
Par. | Description | Value |
a | Distance from CoG to front shaft | 0.758 m |
b | Distance from CoG to rear shaft | 1.036 m |
m | Vehicle mass | 683 kg |
I | Vehicle inertia | 561 kg· |
Tire stiffness coefficient | 15,000 | |
Vehicle drag coefficient | 0.5 | |
A | Vehicle frontal area | 4 m |
Air density at 25 °C | 1.2 | |
Friction coefficient tire-ground | variable |
Installation Parameters of LIDAR Sensor | ||
---|---|---|
Par. | Description | Value |
s | Distance from CoG to sensor on the vehicle longitudinal direction | 0.3 m |
t | Distance from CoG to sensor on the vehicle cross direction | 0.1 m |
Sensor orientation respect to the vehicle longitudinal direction | 90° |
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Vial, P.; Puig, V. Kinematic/Dynamic SLAM for Autonomous Vehicles Using the Linear Parameter Varying Approach. Sensors 2022, 22, 8211. https://doi.org/10.3390/s22218211
Vial P, Puig V. Kinematic/Dynamic SLAM for Autonomous Vehicles Using the Linear Parameter Varying Approach. Sensors. 2022; 22(21):8211. https://doi.org/10.3390/s22218211
Chicago/Turabian StyleVial, Pau, and Vicenç Puig. 2022. "Kinematic/Dynamic SLAM for Autonomous Vehicles Using the Linear Parameter Varying Approach" Sensors 22, no. 21: 8211. https://doi.org/10.3390/s22218211
APA StyleVial, P., & Puig, V. (2022). Kinematic/Dynamic SLAM for Autonomous Vehicles Using the Linear Parameter Varying Approach. Sensors, 22(21), 8211. https://doi.org/10.3390/s22218211