Localization is an essential prerequisite for autonomous robots to execute tasks. In this letter, a multisensor
fused odometry is proposed for the robot applied to dynamic indoor environments. The gyroscope and wheel encoder
information is integrated into visual odometry, where the robot pose is parameterized with 3-D position and 3-D Euler
angle to relieve the estimation drift during planar motion and conveniently fuse wheel encoder measurements. According
to the results of semantic segmentation, the dynamic elements are removed. On this basis, ground plane constraint is
exerted on the optimization procedure. As a result, the estimation accuracy is improved. The experimental results on the
OpenLORIS-scene dataset demonstrate the effectiveness of the proposed odometry.