Visual-Gyroscope-Wheel Odometry With Ground Plane Constraint for Indoor Robots in Dynamic Environment

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.