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.

翻译：轮式移动机器人需要具备估计自身运动及控制行为影响的能力，以实现导航规划。本文提出ST-VIO——一种将轮式地面车辆单轨动力学模型与视觉惯性里程计（VIO）进行紧耦合融合的新方法。该方法在线标定并自适应调整动力学模型，以提升基于未来控制输入的前向预测精度。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受特定控制输入时的运动特性。我们采用无奇点且可微分的单轨模型变体，使其能够作为动力学因子无缝集成至VIO框架中，并与VIO状态变量共同在线优化模型参数。通过在室内外不同地形类型及轮式配置的真实场景数据验证，实验表明ST-VIO不仅能适应车轮或地面条件变化、提升新控制输入下的预测精度，甚至能进一步提高位姿跟踪的准确性。