Research On SLAM Of Mobile Robot Based On Lidar

The new generation of artificial intelligence technology is accelerating its penetration into industries such as manufacturing,and is committed to promoting the transformation and upgrading of the Chinese economy.As a typical representative,intelligent mobile robot have also received considerable attention.Capability of autonomous navigation in complex environments is an important sign of mobile robot intelligence.Simultaneous Localization and Mapping(SLAM)technology is the "eye" of autonomous navigation,which is considered to be a key prerequisite for mobile robots to achieve autonomation and intellectualization.On the basis of the above background,the research on the positioning and mapping technology of lidar-based mobile robots was carried out.The odometer motion model was established in the light of the principle of track estimation on the basis of the completion of the conversion among the robot coordinate system,sensor coordinate system,and world coordinate system.The lidar in combination with odometer are used to complete the error calibration under different sports environments aim at the noise which is caused by the movement of odometer.A lidar observation model based on the likelihood field was established aim at the noise which is caused by the lidar measurement process.Finally,an occupancy grids mapping algorithm is selected as the model formation of environmental map in this paper by analyzing and comparing the advantages and disadvantages of algorithms from mainstream environmental map construction,and the mapping effect of the algorithm is verified with Rviz.Algorithm for mobile robot positioning based on laser scan matching is studied on the basis of the completion of the relevant model construction and data processing work.Various parameters affecting the accuracy of laser scanning matching are selected as factors.Accuracy of algorithm for laser scan matching which is on the basis of iterating the nearest point(ICP)algorithm is analyzed,and MRPT is used for simulation verification in different environments,and the results showed that accuracy of the algorithm meets requirements of indoor positioning.The laser observation model of the latest frame is added to improve accuracy of the sampling and reduce the number of required particles for mapping aimed at the low samplingaccuracy of the RBPF-SLAM algorithm on basis of particle filtering theory after completion of the positioning.At the same time,an optimization design of the RBPF-SLAM algorithm based on the resampling strategy was carried out for the degradation of particle in the resampling process.Finally,it was verified through public datasets,indicating that it can generate a more accurate globally consistent map.Finally,in the light of the experimental platform built by Turtlebot 2 mobile robot and Rplidar A2 lidar,the SLAM system designing and testing based on the ROS framework are implemented,and the SLAM system experiments before and after optimization in different actual environments are completed.The results showed that the optimized SLAM system has better performance in positioning and mapping.