A single frame depth visual gyroscope and its integration for robot navigation and mapping in structured indoor environments

An accurate navigation system is an essential and important part for the mobile robot. The recent appearance of low cost RGBD cameras has made 3D point clouds together with RGB information easy accessible, and they have been widely applied in many applications. Relative poses of a mobile robot can be estimated from consecutive visual information. However, such incremental registration methods still suffer from accumulated errors which makes the estimated trajectory as weird as by only using wheel mounted encoders. In contrast, we introduce a novel and inexpensive sensor fusion based approach to solve the robot localization problem. The key idea is to use visual gyroscope as a complementary source for robot heading estimation. Aided with constraints, the unscented Kalman filter is used for robot pose estimation. A field experiment has been carried out in order to verify the introduced method. Accordingly, the 3D map of the environment is also presented based on the estimated robot trajectory.