Evaluation of non-geometric methods for visual odometry
We stress the advantages of non-geometric (learned) VO as an alternative or an addition to standard geometric methods. Ego-motion is computed with state-of-the art regression techniques, namely Support Vector Machines (SVM) and Gaussian Processes (GP). To our knowledge this is the first time SVM have been applied to VO problem. We conduct extensive evaluation on three publicly available datasets, spanning both indoor and outdoor environments. The experiments show that non-geometric VO is a good alternative, or addition, to standard VO systems.
visual odometry svms gaussian processes autonomous robots ego-motion estimation slam