  Accurate and robust camera tracking in dynamic environments presents a
significant challenge for visual SLAM (Simultaneous Localization and Mapping).
Recent progress in this field often involves the use of deep learning
techniques to generate mask for dynamic objects, which usually require GPUs to
operate in real-time (30 fps). Therefore, this paper proposes a novel visual
SLAM system for dynamic environments that obtains real-time performance on CPU
by incorporating a mask prediction mechanism, which allows the deep learning
method and the camera tracking to run entirely in parallel at different
frequencies such that neither waits for the result from the other. Based on
this, it further introduces a dual-stage optical flow tracking approach and
employs a hybrid usage of optical flow and ORB features, which significantly
enhance the efficiency and robustness of the system. Compared with
state-of-the-art methods, this system maintains high localization accuracy in
dynamic environments while achieving a tracking frame rate of 56 fps on a
single laptop CPU without any hardware acceleration, thus proving that deep
learning methods are still feasible for dynamic SLAM even without GPU support.
Based on the available information, this is the first SLAM system to achieve
this.
