The existence of variable factors within the environment can cause a decline
in camera localization accuracy, as it violates the fundamental assumption of a
static environment in Simultaneous Localization and Mapping (SLAM) algorithms.
Recent semantic SLAM systems towards dynamic environments either rely solely on
2D semantic information, or solely on geometric information, or combine their
results in a loosely integrated manner. In this research paper, we introduce
3DS-SLAM, 3D Semantic SLAM, tailored for dynamic scenes with visual 3D object
detection. The 3DS-SLAM is a tightly-coupled algorithm resolving both semantic
and geometric constraints sequentially. We designed a 3D part-aware hybrid
transformer for point cloud-based object detection to identify dynamic objects.
Subsequently, we propose a dynamic feature filter based on HDBSCAN clustering
to extract objects with significant absolute depth differences. When compared
against ORB-SLAM2, 3DS-SLAM exhibits an average improvement of 98.01% across
the dynamic sequences of the TUM RGB-D dataset. Furthermore, it surpasses the
performance of the other four leading SLAM systems designed for dynamic
environments.

3DS-SLAM 是一种针对动态场景的 3D 语义建图算法，通过 3D 物体检测和动态特征过滤等方法，解决了动态环境下相机定位的精度问题。与其他四个主流动态场景 SLAM 系统相比，3DS-SLAM 在 TUM RGB-D 数据集的动态序列上平均提高了 98.01% 的性能。