Mapping, Localization and Path Planning for Image-based Navigation using Visual Features and Map
This work addresses image-based navigation for robotics or autonomous systems, offering a compact map representation and accurate localization, but it appears incremental as it builds on existing feature representation methods.
The paper tackles the problem of image-based navigation by formulating requirements for map construction and self-localization, using a network flow framework with convex optimization, and shows significant outperformance on indoor and outdoor datasets.
Building on progress in feature representations for image retrieval, image-based localization has seen a surge of research interest. Image-based localization has the advantage of being inexpensive and efficient, often avoiding the use of 3D metric maps altogether. That said, the need to maintain a large number of reference images as an effective support of localization in a scene, nonetheless calls for them to be organized in a map structure of some kind. The problem of localization often arises as part of a navigation process. We are, therefore, interested in summarizing the reference images as a set of landmarks, which meet the requirements for image-based navigation. A contribution of this paper is to formulate such a set of requirements for the two sub-tasks involved: map construction and self-localization. These requirements are then exploited for compact map representation and accurate self-localization, using the framework of a network flow problem. During this process, we formulate the map construction and self-localization problems as convex quadratic and second-order cone programs, respectively. We evaluate our methods on publicly available indoor and outdoor datasets, where they outperform existing methods significantly.