单轨地面车辆动力学模型的在线标定：与视觉惯性里程计的紧耦合融合
Online Calibration of a Single-Track Ground Vehicle Dynamics Model by Tight Fusion with Visual-Inertial Odometry
AI总结：
本文提出ST-VIO，将单轨动力学模型与视觉惯性里程计紧耦合，在线标定模型参数，提升轮式机器人运动预测与跟踪精度。
摘要：
轮式移动机器人需要具备估计自身运动以及控制动作对导航规划影响的能力。本文提出ST-VIO，一种新颖的方法，将轮式地面车辆的单轨动力学模型与视觉惯性里程计（VIO）紧密融合。该方法在线标定并自适应动力学模型，以提高基于未来控制输入的前向预测精度。单轨动力学模型利用常微分方程近似描述轮式车辆在平坦地面上特定控制输入下的运动。我们采用单轨模型的一种无奇异且可微的变体，使其能够作为动力学因子无缝集成到VIO中，并与VIO状态变量一起在线优化模型参数。我们在室内和室外不同地形类型和车轮条件下，使用真实世界数据验证了该方法。实验表明，ST-VIO不仅能适应车轮或地面变化，提高新控制输入下的预测精度，甚至能提升跟踪精度。
英文摘要：
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.
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