  Moving objects can greatly jeopardize the performance of a visual
simultaneous localization and mapping (vSLAM) system which relies on the
static-world assumption. Motion removal have seen successful on solving this
problem. Two main streams of solutions are based on either geometry constraints
or deep semantic segmentation neural network. The former rely on static
majority assumption, and the latter require labor-intensive pixel-wise
annotations. In this paper we propose to adopt a novel weakly-supervised
semantic segmentation method. The segmentation mask is obtained from a CNN
pre-trained with image-level class labels only. Thus, we leverage the power of
deep semantic segmentation CNNs, while avoid requiring expensive annotations
for training. We integrate our motion removal approach with the ORB-SLAM2
system. Experimental results on the TUM RGB-D and the KITTI stereo datasets
demonstrate our superiority over the state-of-the-art.
