  In dynamic environments, performance of visual SLAM techniques can be
impaired by visual features taken from moving objects. One solution is to
identify those objects so that their visual features can be removed for
localization and mapping. This paper presents a simple and fast pipeline that
uses deep neural networks, extended Kalman filters and visual SLAM to improve
both localization and mapping in dynamic environments (around 14 fps on a GTX
1080). Results on the dynamic sequences from the TUM dataset using RTAB-Map as
visual SLAM suggest that the approach achieves similar localization performance
compared to other state-of-the-art methods, while also providing the position
of the tracked dynamic objects, a 3D map free of those dynamic objects, better
loop closure detection with the whole pipeline able to run on a robot moving at
moderate speed.
