  In dynamic scenes, both localization and mapping in visual SLAM face
significant challenges. In recent years, numerous outstanding research works
have proposed effective solutions for the localization problem. However, there
has been a scarcity of excellent works focusing on constructing long-term
consistent maps in dynamic scenes, which severely hampers map applications. To
address this issue, we have designed a multi-level map construction system
tailored for dynamic scenes. In this system, we employ multi-object tracking
algorithms, DBSCAN clustering algorithm, and depth information to rectify the
results of object detection, accurately extract static point clouds, and
construct dense point cloud maps and octree maps. We propose a plane map
construction algorithm specialized for dynamic scenes, involving the
extraction, filtering, data association, and fusion optimization of planes in
dynamic environments, thus creating a plane map. Additionally, we introduce an
object map construction algorithm targeted at dynamic scenes, which includes
object parameterization, data association, and update optimization. Extensive
experiments on public datasets and real-world scenarios validate the accuracy
of the multi-level maps constructed in this study and the robustness of the
proposed algorithms. Furthermore, we demonstrate the practical application
prospects of our algorithms by utilizing the constructed object maps for
dynamic object tracking.
