RGBD-Wheel SLAM System Considering Planar Motion Constraints

Abstract

In this study, a simultaneous localization and mapping (SLAM) system for a two-wheeled mobile robot was developed in an indoor environment using RGB images, depth images, and wheel odometry. The proposed SLAM system applies planar motion constraints performed by a robot in a two-dimensional space to robot poses parameterized in a three-dimensional space. The formulation of these constraints is based on a conventional study. However, in this study, the information matrices that weigh the planar motion constraints are given dynamically based on the wheel odometry model and the number of feature matches. These constraints are implemented into the SLAM graph optimization framework. In addition, to effectively apply these constraints, the system estimates two of the rotation components between the robot and camera coordinates during SLAM initialization using a point cloud to construct a floor recovered from a depth image. The system implements feature-based Visual SLAM software. The experimental results show that the proposed system improves the localization accuracy and robustness in dynamic environments and changes the camera-mounted angle. In addition, we show that planar motion constraints enable the SLAM system to generate a consistent voxel map, even in an environment of several tens of meters.