Navigation system with SLAM-based trajectory topological map and reinforcement learning-based local planner

This paper presents a novel robotic navigation system integrating a visual simultaneous localization and mapping (V-SLAM) based global planner with a deep reinforcement learning (DRL) based local planner. On one hand, map of many modern popular V-SLAM systems is inhomogeneous point cloud, which contains many outliers and is too sparse for reliable global path planning. To address this problem, we propose a novel approach to generate a topological map with both trajectories and map points of V-SLAM. On the other hand, current state-of-the-art (SOTA) DRL-based local planners have shown great efficiency in obstacle avoidance. However, the SOTA DRL-based local planners are sometimes trapped by large obstacles and would fall into some local minimum during training. To address the problems, we propose a sub-target module and a mirror experience replay approach. Test results demonstrate that, our topological map generation is robust against outliers and sparsity of map points of V-SLAM, while our local planner achieves 9.61% success rate of obstacle avoidance higher than the SOTA DRL-based approach. Tests in real environment demonstrate the feasibility of our navigation system.