# Mapping, Localization and Path Planning for Image-based Navigation using Visual Features and Map

## TL;DR

This paper proposes a novel framework for image-based navigation that organizes reference images into a map of landmarks, enabling efficient localization and path planning, with convex optimization formulations and superior performance on datasets.

## Contribution

It introduces a set of requirements for map construction and self-localization, formulated as convex quadratic and second-order cone programs, improving map compactness and localization accuracy.

## Key findings

- Outperforms existing methods on indoor datasets
- Achieves accurate self-localization with compact maps
- Utilizes convex optimization for map and localization problems

## Abstract

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.