  Traditional SLAM algorithms are typically based on artificial features, which
lack high-level information. By introducing semantic information, SLAM can own
higher stability and robustness rather than purely hand-crafted features.
However, the high uncertainty of semantic detection networks prohibits the
practical functionality of high-level information. To solve the uncertainty
property introduced by semantics, this paper proposed a novel probability map
based on the Gaussian distribution assumption. This map transforms the semantic
binary object detection into probability results, which help establish a
probabilistic data association between artificial features and semantic info.
Through our algorithm, the higher confidence will be given higher weights in
each update step while the edge of the detection area will be endowed with
lower confidence. Then the uncertainty is undermined and has less effect on
nonlinear optimization. The experiments are carried out in the TUM RGBD
dataset, results show that our system improves ORB-SLAM2 by about 15% in indoor
environments' errors. We have demonstrated that the method can be successfully
applied to environments containing dynamic objects.
