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：本文提出了一种利用二维激光雷达与里程计在狭窄室内环境中实现UGV（无人地面车辆）稳健快速导航的系统。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。通过在专家演示中注入高斯噪声以增强学习策略的稳健性。我们通过仿真与硬件实验评估了LiCS的性能，其在导航性能方面优于所有其他基线算法，即使在高度杂乱的环境中仍能保持稳健性能。硬件实验表明，LiCS可在最高$1.5\ m/s$的速度下保持安全导航。