  In this paper, we propose a lightweight system, RDS-SLAM, based on ORB-SLAM2,
which can accurately estimate poses and build semantic maps at object level for
dynamic scenarios in real time using only one commonly used Intel Core i7 CPU.
In RDS-SLAM, three major improvements, as well as major architectural
modifications, are proposed to overcome the limitations of ORB-SLAM2. Firstly,
it adopts a lightweight object detection neural network in key frames.
Secondly, an efficient tracking and prediction mechanism is embedded into the
system to remove the feature points belonging to movable objects in all
incoming frames. Thirdly, a semantic octree map is built by probabilistic
fusion of detection and tracking results, which enables a robot to maintain a
semantic description at object level for potential interactions in dynamic
scenarios. We evaluate RDS-SLAM in TUM RGB-D dataset, and experimental results
show that RDS-SLAM can run with 30.3 ms per frame in dynamic scenarios using
only an Intel Core i7 CPU, and achieves comparable accuracy compared with the
state-of-the-art SLAM systems which heavily rely on both Intel Core i7 CPUs and
powerful GPUs.
