Abstract
This project has conducted research on robot path planning based on Visual SLAM. The main work of this project is as follows: (1) Construction of Visual SLAM system. Research has been conducted on the basic architecture of Visual SLAM. A Visual SLAM system is developed based on ORB-SLAM3 system, which can conduct dense point cloud mapping. (2) The map suitable for two-dimensional path planning is obtained through map conversion. This part converts the dense point cloud map obtained by Visual SLAM system into an octomap and then performs projection transformation to the grid map. The map conversion converts the dense point cloud map containing a large amount of redundant map information into an extremely lightweight grid map suitable for path planning. (3) Research on path planning algorithm based on reinforcement learning. This project has conducted experimental comparisons between the Q-learning algorithm, the DQN algorithm, and the SARSA algorithm, and found that DQN is the algorithm with the fastest convergence and best performance in high-dimensional complex environments. This project has conducted experimental verification of the Visual SLAM system in a simulation environment. The experimental results obtained based on open-source dataset and self-made dataset prove the feasibility and effectiveness of the designed Visual SLAM system. At the same time, this project has also conducted comparative experiments on the three reinforcement learning algorithms under the same experimental condition to obtain the optimal algorithm under the experimental condition.
Abstract (translated)
本项目基于视觉SLAM进行了机器人路径规划的研究。本项目的主要工作如下： （1）构建了视觉SLAM系统的基本架构。本项目基于ORB-SLAM3系统开发了视觉SLAM系统，该系统可以进行密集点云映射。 （2）通过地图转换获得了二维路径规划地图。这部分将视觉SLAM系统获得的密集点云地图转换为八叉树映射，然后对网格图进行投影变换。地图转换将包含大量冗余地图信息的密集点云地图转换为极轻的网格地图，适于路径规划。 （3）基于强化学习路径规划算法的 research。本项目对Q-学习算法、DQN算法和SARSA算法进行了实验比较，发现DQN是在高维复杂环境中具有最快收敛速度和最佳性能的算法。本项目在仿真环境中对视觉SLAM系统进行了实验验证。基于开源数据集和自定义数据集的实验结果证明了设计的视觉SLAM系统的可行性和有效性。同时，本项目还在相同实验条件下对三种强化学习算法进行了比较，以获得在实验条件下最优的算法。
URL
https://arxiv.org/abs/2404.14077
