Research on the Improvement of Cartographer Mapping Algorithm for Mobile Robots

In response to the noise impact in 2D laser SLAM mapping algorithms and the problem of map blurring and inaccuracy caused by large errors in mapping loop checks, this paper proposes an improved Cartographer algorithm for mapping. Firstly, in the data fusion stage between IMU and wheel odometer, the original UKF fusion algorithm is improved to AUKF algorithm to update noise parameters in real time and reduce the impact on pose data; Secondly, in the detection stage, a closed-loop detection method based on graph and graph loop matching was proposed. Continuous frames of laser data were constructed into a subgraph for matching to obtain more feature points. The results of the improved algorithm were compared experimentally, and relevant points were selected to calculate the error and make a line graph. It can be intuitively seen that the improved algorithm has a smaller mapping error than before.