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. Supplementary video: https://youtu.be/BuGY1L1FRa4.

翻译：轮式移动机器人需要具备估计自身运动状态及控制动作效果的能力，以进行导航规划。本文提出ST-VIO方法，这是一种将单轨动力学模型与视觉惯性里程计紧耦合的创新方案。该方法能够在线校准和自适应动力学模型，并基于未来控制输入实现精准的前向预测。单轨动力学模型通过常微分方程近似描述轮式车辆在平坦地面上的受控运动。我们采用无奇异点且可微分的单轨模型变体，将其作为动力学因子无缝集成到VIO系统中，并与VIO状态变量共同在线优化模型参数。在室内外不同地形类型与车轮配置的真实场景中验证了该方法。实验结果表明，ST-VIO不仅能适应环境变化并在新控制输入下实现精准预测，甚至能提升跟踪精度。补充视频：https://youtu.be/BuGY1L1FRa4