GOLN: Graph Object-based Localization Network

In the last decades, robotic localization has been mainly addressed with Visual Odometry (VO) or Simultaneous Localization and Mapping (SLAM) approaches, which usually provide an accurate metric precision. Despite the impressive results, these approaches have some shortcomings such as the amount of memory they require and the lack of robustness in non-ideal environments. Inspired by the human capabilities, in this paper we present a novel framework, named Graph Object-based Localization Network (GOLN), to address the topological localization problem with a novel approach, characterized by low memory requirements and robustness with respect to appearance. GOLN is based on a topological map, i.e., a graph, which is fed to a Graph Network (GN) along with global visual features of the environment and returns the estimation of the position node where the robot is located. Experiments have been performed in Unreal Engine (UE4) environments with a simulated ground robot, equipped with a monocular camera.