Online Calibration of a Single-Track Ground Vehicle Dynamics Model by Tight Fusion with Visual-Inertial Odometry

TL;DR

This paper introduces ST-VIO, a method that tightly integrates a single-track vehicle dynamics model with visual-inertial odometry, enabling online calibration and adaptation for improved motion prediction and tracking accuracy in diverse terrains.

Contribution

The paper presents a differentiable, singularity-free single-track model integrated into VIO for real-time calibration and adaptation of vehicle dynamics.

Findings

Enhanced prediction accuracy after online calibration.

Improved tracking accuracy in real-world tests.

Effective adaptation to wheel and ground changes.

Abstract

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.