Active Visual SLAM Based on Hierarchical Reinforcement Learning

Abstract

We present AVS-HRL, a modular Active visual SLAM system based on hierarchical reinforcement learning. The reward function explicitly considers the efficiency of exploration and the accuracy of mapping by utilizing the internal variables of SLAM, such as feature points distribution and loop-closure signal. Compared to end-to-end active SLAM methods, we designed a map reconstruction module that can correct the cumulative error in the incremental mapping process. Furthermore, the inputs of all neural network modules use more abstract and general information, such as grid maps, rather than raw sensor observations. We conducted experiments in two different simulators and real-world environments. In the noisy setting of Habitat environments, our method improves the accuracy of the mapped areas by 68.48% as an average of Gibson and MP3D datasets. Moreover, our method's generalization performance was demonstrated through direct transfer across different simulators and real-world environments.