  Most of the existing visual SLAM methods heavily rely on a static world
assumption and easily fail in dynamic environments. Some recent works eliminate
the influence of dynamic objects by introducing deep learning-based semantic
information to SLAM systems. However such methods suffer from high
computational cost and cannot handle unknown objects. In this paper, we propose
a real-time semantic RGB-D SLAM system for dynamic environments that is capable
of detecting both known and unknown moving objects. To reduce the computational
cost, we only perform semantic segmentation on keyframes to remove known
dynamic objects, and maintain a static map for robust camera tracking.
Furthermore, we propose an efficient geometry module to detect unknown moving
objects by clustering the depth image into a few regions and identifying the
dynamic regions via their reprojection errors. The proposed method is evaluated
on public datasets and real-world conditions. To the best of our knowledge, it
is one of the first semantic RGB-D SLAM systems that run in real-time on a
low-power embedded platform and provide high localization accuracy in dynamic
environments.
