Research of improved RGBD-SLAM algorithm fusing IMU

摘要

Localization based on the RGBD-SLAM may fail and generate great mapping error when collected Kinect feature points of image data are rare or absent.To solve the problem,an improved localization algorithm was proposed based on the inenial sensor(IMU),the somatosensory sensor(Kinect)and the motion state of the robot itself.The comparison and fusion were done for attitudes,at the same time,the prediction model and the observation model were constructed using IMU measurement data and results of Kinect pose estimation for position respectively,the robot's movement and motion commands restrictions were taken as constraints to do the extended Kalman filter(extended Kalman filter,EKF)integration for robot localization and mapping.Experimental results show that the method can improve the localization accuracy of the robot and the effect of mapping based on the RGBD-SLAM algorithm.