Research On SLAM Technology Of Indoor Medical Mobile Robot Based On Lidar

As a kind of mobile platform widely used in medical environment,medical mobile robot is of great significance in replacing medical staff to work in highly infected areas and high radiation scenes.Especially during the epidemic period,the use of medical mobile robots to assist medical staff to complete the distribution of medical supplies and disinfection of crowd gathering areas can not only avoid the risk of infection of medical staff,but also alleviate the psychological burden of medical staff to a certain extent.In addition,medical mobile robots can help medical staff transport large-scale medical devices when there are heavy tasks in hospital departments.SLAM(Simultaneous Localization And Mapping),known as real-time positioning and mapping,is a key technology for medical mobile robot to realize intelligent and autonomous movement in indoor medical environment.In this project,the medical mobile robot uses the odometer unit for real-time positioning,and combines with the lidar sensor to obtain the obstacle information of the surrounding environment space in real time,so as to complete the map construction of the robot environment space.Aiming at the problems that the medical mobile robot cannot accurately express the environment map due to the accumulation of odometer positioning error and map distortion in the indoor environment.Therefore,the localization and mapping of mobile robot in SLAM process are studied and optimized in this paper.At the same time,particle filtering SLAM algorithm is introduced to build the indoor environment map,and on this basis,the pose,weight and resampling function of particles are improved.The experimental results show that the improved algorithm is more accurate and more robust.The contributions and innovations of this paper are as follows:(1)Designed a medical ultraviolet disinfection mobile robot,which can carry out 360°sterilization without dead angle in indoor medical environment.It uses STM32F103 as the main control unit,which is responsible for collecting all kinds of sensor information,and controls the translation and rotation of the medical mobile robot through the driver.(2)In order to overcome the error accumulation of odometer unit caused by long-time SLAM of medical mobile robot,a real-time data fusion method of odometer unit and lidar sensor is studied.In this method,the acquired obstacle information is converted into the pose change of the lidar sensor itself,and then fused with the real-time positioning information obtained by the odometer unit.(3)In order to overcome the map distortion caused by high-speed rotation,slipping and other factors of medical mobile robot in motion,an inter frame matching method of laser point set based on adjacent sampling consistency was studied to solve the problem of map motion distortion.In this method,the laser point sets of each frame acquired by the lidar sensor at different times are matched,and the noise is reduced by the consistent filtering of adjacent laser points.(4)In order to reduce the noise interference during indoor environment map construction and the requirements of subsequent navigation tasks on map accuracy,a mean matching gaussian noise function is studied to represent the optimal particle pose based on the framework of particle filtering SLAM algorithm.In order to prevent the noise interference accumulated by long-term SLAM of medical mobile robots,which would greatly reduce the weight of most particles,this paper studies a particle weight matching optimization method to maintain the weight value of particle swarm,and proposes an adaptive hierarchical resampling function to maintain the diversity of particle weight value.Through the research and improvement of the above methods,and based on a large number of experimental test results,it is shown that the proposed method can effectively improve the accumulation of positioning errors of the odometer unit and eliminate the distortion of the map.The improved SLAM algorithm framework based on particle filtering results show that the improved method makes the estimated pose of particle swarm closer to the real value of mobile robot,and the degradation degree of the particle swarm is effectively improved.