Research On Pose Estimation Of Mobile Robot Based On Vision Sensor

Obtaining own posture information is a prerequisite for mobile robots to realize navigation functions such as path planning.The use of traditional positioning sensors is prone to inaccurate positioning under certain special circumstances.For example,the odometer is prone to slip on uneven ground.IMU is prone to serious drift when the robot does 3D variable speed movement for a long time.GPS in the indoor environment,the phenomenon of poor positioning is prone to occur.However,the visual sensor can obtain rich environmental information,and is not limited by wheel slip and other factors,to a certain extent,it can solve the problem of inaccurate positioning of traditional positioning sensors under specia l circumstances.Based on this,this paper studies the pose estimation method of mobile robot based on visual sensor,the main work is as follows:(1)Positioning requires real-time estimation of the pose of the robot.It is coupled with the mapping problem and constitutes the problem of simultaneous localization and mapping(SLAM).Therefore,this paper first studies the pure visual SLAM system based on monocular camera and depth camera,and then the problem that the sparse point cloud image cannot display some environment information around the mobile robot and the posture trajectory estimated by the monocular camera lacks the absolute scale is improved.In view of the problem that sparse point cloud maps cannot display some environmental information,this paper adds a module to build a dense point cloud map through point cloud stitching based on the existing depth camera-based SLAM system.Aiming at the problem that the posture trajectory estimated by the monocular camera lacks absolute scale,this paper build s a visual-inertial fusion system based on previous work.The system has loopback detection and relocation functions,and marginalizes frames far away from the current,enhancing the accuracy and real-time performance of the system.(2)Experiments and precision analysis were carried out on the pure visual SLAM system and the visual-inertial fusion SLAM system in the data set and the real scene respectively.The experimental results show that the local and global positioning accuracy of the SLAM system based on the depth camera is relatively high,and the constructed dense point cloud map can display more environmental information around the mobile robot and is more intuitive.The visual-inertial fusion SLAM system built in this paper can restore the posture and trajectory scale information that the monocular camera lacks.The accuracy of the posture estimation is close to the accuracy of the pure monocular visual system that can ideally restore the true scale.Compared with the current mainstream VINS-Mono system,the absolute trajectory estimation error is smaller,and the average tracking time of the system and the performance of the system in different sequences of the data set show that the system constructed in this paper has good real-time and robustness.