Research On Robot Indoor Navigation Technology Based On Laser SLAM

Nowadays,as the most sophisticated navigation technology,GPS can’t solve the problems of navigation in many complex indoor environments.As the fusion of two important elements in navigation,Simultaneous Localization and Mapping(SLAM),is really important to navigation.Therefore,this article mainly analyzes and improves the technology of indoor navigation based on laser SLAM algorithm.We improve its back-end optimization and closed-loop detection part under the framework of graph-based optimization.A series of indoor navigation experiments for mobile robots including dynamic obstacle avoidance and fixed-point cruise are realized based on the robot platform.The specific research content is as follows:Firstly,after researching and deriving the 2D-SLAM algorithm based on lidar,the shortcoming of the filtering-based algorithm derived from the Gmapping algorithm is:because its mathematical principle is based on Markov hypothesis,only the conversion between adjacent data is performed when processing data,and doesn’t process the error.As a result,the error will increase with the accumulation of data and affect the accuracy of the map.Therefore,a mathematical framework based on optimization is proposed to implement SLAM algorithms.For the optimized Hector-SLAM algorithm,when the moving speed of the mobile robot is too fast,the data may not be updated in time.It will cause the map to appear ghosting,blurring,etc.,which affects the mapping accuracy.An improved method is proposed,which is to use the bicubic interpolation algorithm in image processing.It can process raster maps better,solves the problems of some burrs and ghosts,and gets more accurate maps.Secondly,in view of the current commonly used laser SLAM algorithm,there are limitations in closed-loop detection.In some geometrically symmetric environments,false alarms are prone to cause false closed-loops and reduce the accuracy of mapping.So we add the Lazy Decision algorithm to the closed loop detection.Through the threshold which is set advanced,the closed loop is performed after the threshold is established when the map is built.It reduces the number of closed loops and the complexity of calculation,and effectively improves the accuracy of building the map.Thirdly,based on the back-end optimization part of the graph optimization framework,a laser SLAM algorithm for sparse pose optimization is proposed.It uses the LM algorithm as the framework,and the sparse system is processed through Cholesky decomposition.The algorithm is accelerated by the sparse structure of the matrix.The optimization algorithm takes into account the covariance information contained in the constraints.In every iteration,the optimization algorithm will be linearized relative to all constraints of the current pose,which improves the accuracy and has a very fast convergence speed,which can effectively reduce memory consumption and increase SLAM mapping accuracy.Finally,the navigation framework and implementation method of the robot room are described.Aiming at the complex environment of the robot’s indoor navigation,a four-wheel differential mobile robot is used as the main body and the Robot Operation System(ROS)is used as the operating system to independently establish an experimental platform.Through this platform,the comparative experiments among the SLAM algorithm based on sparse pose optimization and the three algorithms of Gmapping and Hector in different environments are completed.According to the experimental results,the improved algorithm has the highest mapping accuracy and the lowest memory consumption.On this basis,a series of indoor navigation experiments for mobile robots including dynamic obstacle avoidance and fixed-point cruise are realized.