Research On Back-End Optimization Algorithm Of Mobile Robot SLAM

Simultaneous localization and mapping(SLAM)has received extensive attention from researchers as an important technology for robot development,and back-end optimization as an important component of SLAM has also been widely studied.Since back-end optimization faces a large amount of data to be optimized,the optimization process suffers from computational instability,low efficiency,and poor system robustness,leading to unsatisfactory results.In order to further improve the accuracy and promote the intelligent application and development of robots,this paper investigates the SLAM back-end optimization algorithm.This paper investigates the bundle adjustment(BA)in the SLAM back-end graph optimization method.The Jacobi matrix generated by the LM iterative method in BA solving process may be singular,resulting in singular or ill-conditioned algorithms.To address this problem,an improved LM algorithm is proposed in this paper.By defining the calculation of the iterative parameters,the previous iteration result is introduced into the next iteration calculation,which can reduce the problem that the function is large when the current solution is far away from the solution set.And at the same time,it can have second-order convergence without assuming that the Jacobi matrix is non-singular,and the computational efficiency is improved while ensuring the stability of the algorithm.Experimental results show that the improved LM algorithm can effectively improve the computational efficiency.Under the condition of achieving the same result,the number of iterations is reduced by 23.53%and 13.33%compared with the LM and C-LM algorithms;Compared with LM-BA and C-LM-BA.I-LM-BA has the smallest error for trajectory map optimization,and the burr of the trajectory graph in optimization is significantly reduced and more stable.Since there are outliers in the data to be optimized due to misidentification and other reasons,which reduces the optimization accuracy,the optimization algorithm for back-end robustness is investigated in this paper.Aiming at the problems of computational complexity and low optimization accuracy in typical algorithms,an adaptive dynamic covariance scaling(ADCS)algorithm is proposed.The ADCS algorithm only needs to calculate the nodes,and then dynamically update the value of the parameter S and φ based on the derived calculation formula and each iteration result,which can simplify the calculation while effectively attenuating the impact of the error closed loop on the overall optimization and achieving the robustness of the back end.The ADCS algorithm is applied to different datasets for simulation experiments.The results show that the ADCS algorithm can still complete the high-precision estimation graph optimization for the datasets with error closed loop added,and realize the back-end robustness.It can simplify the calculation process and reduce the running time by 28.05%and 7.65%compared with the DCS and CPS algorithms.It also can complete the high-precision trajectory graph optimization for different datasets,and compared with the more accurate CPS algorithm,this algorithm reduces the optimization errors of RingCity and Manhattan datasets by 11.36%and 22.18%.