LiDAR-Based End-to-End Active SLAM Using Deep Reinforcement Learning in Large-Scale Environments

Autonomous exploration in expansive and complicated environments poses a significant challenge. When the dimensions of the environment expand, exploration algorithms encounter substantial overhead, which can overpower the computational capacity of mobile platforms. In this paper, we propose a novel 3D LiDAR-based end-to-end autonomous exploration network architecture, which allows mobile robots to learn to explore autonomously in expansive environments through deep reinforcement learning. Specifically, we utilize both scans from the LiDAR sensor and maps obtained by SLAM as exploration information to predict the robot's linear and angular actions simultaneously. Furthermore, in order to enhance exploration capability, intrinsic rewards are also used during training. Compared to the existing methods, our proposed approach demonstrates improved learning efficiency and adaptability for various environments. Moreover, the proposed method can complete exploration in unknown environments with a shorter trajectory length than state-of-the-art methods. Additionally, experiments are conducted on the physical robot. which indicates that the trained network can be seamlessly transferred from the simulation to the real world.