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：视觉-惯性里程计（VIO）是适用于功率和载荷受限自主机器人的重要技术。本文提出一种新型立体相机VIO方法，该方法在线集成并标定基于速度控制的车轮式移动机器人运动学运动模型。引入此类运动模型有助于提升VIO精度。与多项旨在集成轮式里程计测量值的既有方法相比，本方法无需轮式编码器，且适用于可通过基于速度控制的运动学运动模型建模的机器人运动场景。我们采用径向基函数（RBF）核来补偿控制指令与实际机器人运动之间的时间延迟与偏差。该运动模型由VIO系统在线标定，可作为前向模型用于运动控制与规划。我们利用不同尺度室内环境采集的数据评估该方法，证明其相比纯VIO方法的性能提升，并评估了在线标定模型的预测精度。