For the positioning task of mobile robots in indoor environment， the emerging auxiliary positioning technology based on Visual Inertial Odometry （VIO） is heavily limited by the light conditions and cannot works in the dark environment. And Ultra-Wide Band （UWB）-based positioning methods are easily affected by Non-Line Of Sight （NLOS） error. To solve the above problems， an indoor mobile robot positioning algorithm based on the combination of UWB and VIO was proposed. Firstly， S-MSCKF （Stereo-Multi-State Constraint Kalman Filter） algorithm/DS-TWR （Double Side-Two Way Ranging） algorithm and trilateral positioning method were used to obtain the position information of VIO output/positioning information resolved by UWB respectively. Then， the motion equation and observation equation of the position measurement system were established. Finally， the optimal position estimation of the robot was obtained by data fusion carried out using Error State-Extended Kalman Filter （ES-EKF） algorithm. The built mobile positioning platform was used to verify the combined positioning method in different indoor environments. Experimental results show that in the indoor environment with obstacles， the proposed algorithm can reduce the maximum error of overall positioning by about 4.4% and the mean square error of overall positioning by about 6.3% compared with the positioning method only using UWB， and reduce the maximum error of overall positioning by about 31.5% and the mean square error of overall positioning by about 60.3% compared with the positioning method using VIO. It can be seen that the proposed algorithm can provide real-time， accurate and robust positioning results for mobile robots in indoor environment.