Research on the Mobile Robot Map-Building Algorithm Based on Multi-Source Fusion

Abstract

In this paper, the mobile robot position fusion algorithm is inaccurate. There is a delay, and the map-construction accuracy is not high; an improvement method is proposed. First, the Cartographer algorithm is optimized. Radius filtering is used for data processing after voxel filtering. In contrast, the idea of multi-sensor fusion is used to fuse the processed IMU data information. This improved method improves the efficiency of the algorithm and the accuracy of the positional pose fusion. We verify the effect of the algorithm applied to the environment map, respectively, in the experimental building promenade environment and the teaching building hall environment, and analyze and compare the effect of map construction before and after the improvement; the experiment proves that in the experimental building promenade environment, the absolute error of measuring and analyzing the obstacles reduces by 0.06 m, and the relative error decreases by 1.63%; in the teaching building hall environment, the absolute error of measuring and analyzing the longest side of the map decreases by 1.121 m and the relative error decreased by 5.52%. In addition, during the experimental operation, the CPU occupancy of the optimized algorithm is around 59.5%. In contrast, the CPU occupancy of the original algorithm is 67% on average, and sometimes it will soar to 75%. The experimental results prove that the algorithm in this paper significantly improves performance in all aspects when constructing real-time environment maps.

Keywords

• lidar
• simultaneous localization and map-building
• multi-source fusion
• mobile robotics
• radius filtering