In this letter, we propose a robust and fast navigation system in a narrow indoor environment for UGV (Unmanned Ground Vehicle) using 2D LiDAR and odometry. We used behavior cloning with Transformer neural network to learn the optimization-based baseline algorithm. We inject Gaussian noise during expert demonstration to increase the robustness of learned policy. We evaluate the performance of LiCS using both simulation and hardware experiments. It outperforms all other baselines in terms of navigation performance and can maintain its robust performance even on highly cluttered environments. During the hardware experiments, LiCS can maintain safe navigation at maximum speed of $1.5\ m/s$.

翻译：本文提出一种基于二维激光雷达与里程计的UGV（无人地面车辆）狭窄室内环境鲁棒快速导航系统。我们采用基于Transformer神经网络的行为克隆方法，学习基于优化的基线算法。在专家演示过程中注入高斯噪声以增强学习策略的鲁棒性。通过仿真与硬件实验评估LiCS的性能，其在导航性能方面优于所有基线方法，即使在高度杂乱环境中仍能保持鲁棒性能。硬件实验表明，LiCS可在最高$1.5\ m/s$速度下保持安全导航。