A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is
Indoor 3D Semantic Robot VSLAM Based on Mask Regional Convolutional Neural Network
During the construction of indoor environmental semantic maps by robot Vision SLAM (VSLAM), there exist some problems such as low label classification accuracy and low precision under the situation of sparse feature points. In this case, this paper proposes an indoor three-dimensional semantic VSLAM algorithm based on Mask Regional Convolutional Neural Network (RCNN). Firstly, an Oriented FAST and a Rotated BRIEF (ORB) algorithms are used to extract image feature points. Secondly, a Randomdoi:10.1109/access.2020.2981648 fatcat:jlkkeri6cvgfxb5mgmczi3sjim