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. Our method
calibrates and adapts the dynamics model online and facilitates accurate
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 our experiments, we demonstrate that our ST-VIO
can not only adapt to the change of the environments and achieve accurate
prediction under new control inputs, but even improves the tracking accuracy.

本文提出了 ST-VIO，一种将单轨道动力学模型与视觉惯性测距紧密融合的新方法，可以对轮式地面车辆的运动进行估计，并根据未来的控制输入进行准确的前向预测。通过在线校准和调整动力学模型，该方法在室内外不同环境中使用真实数据进行了验证，证明了 ST-VIO 可以适应环境的变化并实现准确的预测，甚至可以提升追踪精度。