Virtual Maps for Autonomous Exploration with Pose SLAM We consider the problem of autonomous mobile robot exploration in an unknown environment taking into account the robotxe2x80x99s mapping rate, map uncertainty, and state estimation uncertainty. This paper presents an exploration framework built upon segmentaided pose SLAM adapted for better active localization. We build on our previous work on expectation maximization (EM) exploration, which explicitly models unknown landmarks as latent variables and predicts their expected uncertainty, to resolve the lack of landmark state in denser instances of SLAM. The proposed system comprises path generation, place recognition forecasting, belief propagation and utility evaluation using a virtual map. We analyze the performance in simulated experiments, showing that our algorithm maintains higher coverage speed in exploration as well as lower mapping and localization error. The realtime applicability is demonstrated on an unmanned ground vehicle.

Active SLAM using Connectivity Graphs as Priors Mobile robots can be considered completely autonomous if they embed active algorithms for Simultaneous Localization And Mapping (SLAM). This means that the robot is able to autonomously, or actively, explore and create a reliable map of the environment, while simultaneously estimating its pose. In this paper, we propose a novel framework to robustly solve the active SLAM problem, in scenarios in which some prior information about the environment is available in the form of a topometric graph. This information is typically available or can be easily developed in industrial environments, but it is usually affected by uncertainties. In particular, the distinguishing features of our approach are: the inclusion of prior information for solving the active SLAM problem; the exploitation of this information to pursue active loop closure; the online correction of the inconsistencies in the provided data. We present some experiments, that are performed in different simulated environments: the results suggest that our method improves on stateoftheart approaches, as it is able to deal with a wide variety of possibly large uncertainties.