Visual SLAM is constrained by a strong static world assumption,
making it hardly succeed when dynamic objects appear. Although some methods
eliminate dynamic objects by combining semantic and geometric information, the
fewer classes detected by the semantic method can not benefit these systems by
applying more scenes and constructing a richer semantic map. Our paper raises a
semantic visual SLAM system for a motion scene, which can run in real-time. We
use object detection to detect 80 classes in the scene and the moving consistency
checking to find outliers in every image. We propose different methods to check
the motion state of humans and other objects separately. For the detected human,
we propose an algorithm that judges if a person is sitting and divides the bounding
box belonging to the sitting person into two parts according to the proportion of
the human body. Then we use the same threshold as checking the boxes of the
standing person to determine the state of the two boxes belonging to the sitting
person, respectively. For other different objects except for humans, we propose an
algorithm that automatically adjusts the threshold of different bounding boxes.
This way, it can have the same detection performance for different objects. Finally,
we retain the static box points contained in the dynamic box and eliminate other
points in the dynamic box to benefit system performance from more detected
classes. We evaluated our SLAM on the TUM RGB-D dataset. The performance
of our SLAM exceeds most SLAM systems in a dynamic environment. Our system
is also tested in a real environment with a monocular camera to show its robustness
and universality.
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