G2o-based SLAM rear end optimization algorithm method

The invention discloses a g2o-based SLAM rear end optimization algorithm method, pose node information of a robot is used as input data, a weight factor is added for each edge by focusing closed loop restriction with a rear end optimization algorithm, a value of the weight factor is obtained according to a mathematic relation between the derived weight factor and an information matrix, and after the read end optimization algorithm, the pose node information of the robot is corrected; a built-in optimization strategy of g2o is further optimized based on a g2o platform by a least square method, and a post node path more according with an actual path condition is constructed. The g2o-based SLAM rear end optimization algorithm method adopts a DCSI algorithm, and is used for solving the problem of robutness rear end optimization, complexity is reduced, operation time is reduced and the rate of convergence rate is improved. The g2o-based SLAM rear end optimization algorithm method has important meaning in correction and optimization of a topological map in an unknown environment.