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 robot's mapping rate, map uncertainty, and state estimation uncertainty. This paper presents an exploration framework built upon segment-aided 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 real-time applicability is demonstrated on an unmanned ground vehicle.