  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.
