Research On Simultaneous Location And Mapping Based On ROS For Service Robots

With the development of economy,the increase of labor costs and the aging of population,service robots have developed rapidly in recent years.The intelligence of service robots is mainly reflected in the ability of autonomous navigation,and the key technology in autonomous navigation is SLAM(Simultaneous Location and Mapping),therefore,the paper focuses on the research of the service robots SLAM algorithm under the ROS platform.Firstly,the service robot experiment platform is built according to the navigation requirements.On the hardware platform,the main controller,odometer,obstacle avoidance sensor and drive motor are selected,as well as circuit design.The main controller selects Intel i5 mini host;the obstacle avoidance sensor module uses laser radar,ultrasonic ranging sensor and kinect camera;the obstacle avoidance sensor module uses a lidar and a kinect camera;the hardware circuit part mainly develops the motor drive circuit design.On the software system,complete the communication between the ROS platform and the embedded platform,and the data acquisition of the lidar and so on.Secondly,the modules of the service robot SLAM system are modeled.In order to facilitate the mathematical processing and optimization of the calculation,the SLAM probability model is established;in order to facilitate the analysis,it is assumed that the center of gravity of the robot is located on the center of the chassis to build a simplified model of the service robot;the Cartesian coordinate system is used to construct the robot coordinate system model;in order to predict the change of robot pose,a robot motion model is established.;the odometer model and the sensor observation model are constructed according to the encoder and the laser radar;in order to visually observe the construction effect,the grid map model is selected as the map type in the paper.Then,the SLAM method based on probability estimation is studied.Two optimized SLAM algorithms based on Bayesian filtering are analyzed: SLAM algorithm based on extended Kalman filter and FastSLAM algorithm.Using matlab to simulate and analyze the two algorithms,the FastSALM algorithm with better performance is determined as the basic algorithm for simultaneous location and mapping.It is improved by fusion fading adaptive unscented particle filtering and Gaussian resampling.The algorithm performance was tested in simulation environment,and the numerical test result shows that,compared with the FastSLAM2.0 and UFastSLAM in different particle numbers and noise,the estimated accuracy of the improved algorithm is increased by an average of 53.6% and 21.3%,system stability increased by an average of 31% and 16.5%,which verifies the effectiveness of the algorithm.Finally,based on the navigation requirements of the service robot and the improved FastSLAM algorithm analysis,the simultaneous location and mapping based on Gmapping algorithm is realized in the Gazebo simulation environment;Then,on the constructed map,the A* algorithm of global path planning,the dynamic window method of local path planning and the adaptive Monte Carlo positioning algorithm are used to realize the autonomous navigation of the service robot;Finally,the verification of robot mapping and navigation experiments is carried out in the actual environment.The experimental results show that the navigation algorithm in this paper can meet the needs of autonomous navigation of service robots.