Comparison of stereo visual inertial odometry algorithms for Unmanned Ground Vehicles

For its research it emerged the need to investigate indoor localization algorithms, in particular the visual-inertial type. This work aims to study different types of algorithms to assess which one is the best choice for indoor localization with the already available COTS hardware. Although the end application is meant to be UAV, a Jackal UGV is used instead, because it was the vehicle available and it lowered the risks of damages during the testing phase. A MYNTYEYE S stereo camera, with included IMU and IR projector, was available and mounted on the UGV. Three algorithms are considered: a light-weight filter-based VIO framework, ROVIO, and two optimization-based VIO frameworks, VINS-Fusion and OKVIS, that should better accommodate data asynchrony. The algorithms are tested in two different environments making the robot follow two paths multiple times: a linear path in a corridor and a pseudo-rectangular one in a room. The algorithms performance is evaluated by two parameters: the relative error on the total travelled distance and the difference between initial and final position of the UGV. The tests underlined no best algorithm, but a dependence on the environment. As future work, it would be interesting to test a camera using a tight hardware-synchronization with an IMU, since, according to the literature, should be a big source of error.