Omnidirectional visual-inertial odometry using multi-state constraint Kalman filter

We present an Omnidirectional Visual-Inertial Odometry (OVIO) approach based on Multi-State Constraint Kalman Filtering (MSCKF) to estimate the ego-motion of a moving platform. Instead of considering visual measurements on image plane, we use individual planes for each point that are tangent to the unit sphere and normal to the corresponding measurement ray. This way, we combine spherical images captured by omnidirectional camera with inertial measurements within the filtering method MSCKF. The key hypothesis of OVIO is that a wider field of view allows incorporating more visual features from the surrounding environment, thereby improving the accuracy and robustness of the motion estimation. Moreover, by using an omnidirectional camera, it is less likely to end up in a situation where there is not enough texture. We provide an evaluation of OVIO using synthetic and real video sequences captured by a fish-eye camera, and compare the performance with MSCKF using a perspective camera. The results show the superior performance of the proposed OVIO.