Visual-inertial odometry (VIO) is an important technology for autonomous robots with power and payload constraints. In this paper, we propose a novel approach for VIO with stereo cameras which integrates and calibrates the velocity-control based kinematic motion model of wheeled mobile robots online. Including such a motion model can help to improve the accuracy of VIO. Compared to several previous approaches proposed to integrate wheel odometer measurements for this purpose, our method does not require wheel encoders and can be applied when the robot motion can be modeled with velocity-control based kinematic motion model. We use radial basis function (RBF) kernels to compensate for the time delay and deviations between control commands and actual robot motion. The motion model is calibrated online by the VIO system and can be used as a forward model for motion control and planning. We evaluate our approach with data obtained in variously sized indoor environments, demonstrate improvements over a pure VIO method, and evaluate the prediction accuracy of the online calibrated model.
翻译：本文提出了一种新颖的Stereo眼视觉-惯性里程计（VIO）方法，使用立体相机集成和校准轮式移动机器人速度控制的基于运动模型的运动模型。包括这样一个运动模型可以帮助提高VIO的精度。与几种先前的方法相比，这些方法用于集成轮子编码器测量的目的，我们的方法不需要轮子编码器，并且可以在运动可以使用基于速度控制的运动模型进行建模时使用。我们使用径向基函数（RBF）核来补偿控制命令与实际机器人运动之间的时间延迟和偏差。该运动模型由VIO系统在线校准，可以用作运动控制和规划的前向模型。我们使用在各种室内环境中获得的数据来评估我们的方法，展示了纯VIO方法的改进，并评估在线校准模型的预测精度。
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