Study On Technology Of SLAM Based On RGB-D Camera

With the development of science and technology,mobile robots have gradually entered people's lives and become a very important part.At present,the requirements for robots in various fields include the autonomous positioning and path planning of robots.To achieve these functions,we must first realize the simultaneous localization and mapping of the robot,namely SLAM.The system of SLAM can complete the robot's autonomous positioning and environment map construction through the environmental information obtained by the sensor when the environment of the robot is unknown.This paper studies the visual SLAM system based on RGB-D camera.The main research work is as follows.(1)A classic framework of visual SLAM is introduced,then is the complete circuit of the SLAM system based on KINECT.And the structure,principle and imaging mode of KINECT is also introduced.The KINECT is calibrated so that the internal and external parameters of the camera are obtained.(2)The realization of the visual odometer at the front of the SLAM system is based on the method of feature.The image features are selected as ORB(Oriented FAST and Rotated BRIEF)features.Scale invariance and rotation invariance are added to the ORB feature by image pyramid and gray centroid method.Then,the Hamming distance is used as the metric for feature matching.To eliminate the mismatching in the matching result,the distance threshold is set and the suboptimal matching is used.The experimental results show that the correct feature matching result can be obtained after using the method of eliminating the mismatch above.Then,the method of EPnP(Efficient Perspective-n-Point)is used to estimate the motion of the camera between two adjacent frames,and then the estimated pose is optimized by the method of graph optimization.(3)The function of loop closing,global optimization and environment map construction of the system is implemented.The concept of key frame is defined first.The key frame is selected based on the bag of words to perform loop closing detection so that problem of position drift caused by error accumulation can be solved.The global pose optimization is performed using graph optimization after the loop closing,and then the globally consistent trajectories and maps are constructed.(4)In order to verify the validity and practicability of the system in this paper,experiments were carried out using datasets and real environment.First,the experiments in the TUM dataset are used to conduct experiments.The trajectories estimated before and after the global optimization are compared with the real trajectories,and the errors of the trajectories are calculated.The experimental results show that the accuracy of positioning is enhanced and the effect of pose tracking is better after the global optimization of the system.The results show that the system can construct maps in real time,whether it is used in dataset or real environment,and the constructed maps can be applied to positioning and navigation,which verifies the validity and practicability of the system.