Evaluation of non-geometric methods for visual odometry

Visual Odometry (VO) is one of the fundamental building blocks of modern autonomous robot navigation and mapping. While most state-of-the-art techniques use geometrical methods for camera ego-motion estimation from optical flow vectors, in the last few years learning approaches have been proposed to solve this problem. These approaches are emerging and there is still much to explore. This work follows this track applying Kernel Machines to monocular visual ego-motion estimation. Unlike geometrical methods, learning-based approaches to monocular visual odometry allow issues like scale estimation and camera calibration to be overcome, assuming the availability of training data. While some previous works have proposed learning paradigms to VO, to our knowledge no extensive evaluation of applying kernel-based methods to Visual Odometry has been conducted. To fill this gap, in this work we consider publicly available datasets and perform several experiments in order to set a comparison baseline with traditional techniques. Experimental results show good performances of learning algorithms and set them as a solid alternative to the computationally intensive and complex to implement geometrical techniques. 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.