Online Calibration of a Single-Track Ground Vehicle Dynamics Model by Tight Fusion with Visual-Inertial Odometry
This work addresses motion estimation and control planning for wheeled robots, but it is incremental as it builds on existing VIO and dynamics modeling techniques.
The authors tackled the problem of improving motion prediction for wheeled mobile robots by tightly fusing a single-track dynamics model with visual-inertial odometry (VIO) to calibrate and adapt the model online, resulting in improved accuracy for forward prediction and even tracking in real-world experiments across various terrains.
Wheeled mobile robots need the ability to estimate their motion and the effect of their control actions for navigation planning. In this paper, we present ST-VIO, a novel approach which tightly fuses a single-track dynamics model for wheeled ground vehicles with visual inertial odometry (VIO). Our method calibrates and adapts the dynamics model online to improve the accuracy of forward prediction conditioned on future control inputs. The single-track dynamics model approximates wheeled vehicle motion under specific control inputs on flat ground using ordinary differential equations. We use a singularity-free and differentiable variant of the single-track model to enable seamless integration as dynamics factor into VIO and to optimize the model parameters online together with the VIO state variables. We validate our method with real-world data in both indoor and outdoor environments with different terrain types and wheels. In experiments, we demonstrate that ST-VIO can not only adapt to wheel or ground changes and improve the accuracy of prediction under new control inputs, but can even improve tracking accuracy.