## 6 Conclusions  
In this paper, a new map-building algorithm is proposed for the inaccuracy of Cartographer algorithm position fusion, the existence of dense point-cloud data with high computational volume, and the low accuracy and efficiency of map construction. To reduce the diffusivity of the point-cloud data after voxel filter processing, this paper adopts the secondary filtering method of PCL library radius filtering. By setting the thresholds for the filter radius and the number of point clouds, the point clouds that do not satisfy the threshold conditions within the specified radius range are effectively filtered out, accelerating the speed of point-cloud preprocessing and improving the algorithm's efficiency. At the same time, the multi-sensor-fusion method is adopted to fuse the IMU data information with the algorithm after preprocessing to enhance the effectiveness of the front-end map construction and the back-end loopback detection and ultimately to realize the construction of high-precision maps. By comparing and analyzing the experimental results of the original algorithm and the improved algorithm to construct the map, in the environment of the long corridor of the experimental building, the absolute error of measuring and analyzing the obstacles is reduced by 0.06 m, and the relative error is decreased by 1.63%; in the environment of the hall of the teaching building, the absolute error of measuring and analyzing the longest side of the map is reduced by 1.121 m. The relative error is decreased by 5.52%. In addition, during the experimental run, the CPU occupancy of the optimized algorithm is around 59.5%, while the CPU occupancy of the original algorithm averages 67% and sometimes spikes to 75%. This paper concludes that the fusion of inertial sensors based on the improved algorithm can improve the efficiency and accuracy of map-building. This paper concludes that the fusion of inertial sensors based on improved algorithms can improve the efficiency and accuracy of map-building.  
In the future, in addition to the algorithms in this paper, there are sub-tasks such as loopback detection, position recovery, and sliding window optimization that plays an optimization role for map-building and localization in SLAM tasks--integrating these sub-tasks in the priority definition algorithm and network inference model to reach multi-task joint learning and realize the unity of multi-modal, multi-task, and real-time. Joint multi-task learning with correlation can also improve the performance of localization and map-building, which is one of the sub-directions worth studying.  
Conceptualization, B.X. and Z.Y.; Methodology, B.X. and Z.Y.; writing--original draft preparation, Z.Y., B.X. and L.Z.; writing--review and editing, B.X., Z.Y. and W.W.; supervision, B.X., Z.Y., L.Z. and W.W.; project administration, B.X., L.Z. and W.W.; funding acquisition, B.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Shanghai Science and Technology Committee (STCSM) Local Universities Capacity-building Project (No. 22010502200).  
Not applicable.  
The data are available on request.  
The authors would like to express their gratitude for the support of Fishery Engineering and Equipment Innovation Team of Shanghai High-level Local University.  
The authors declare no conflict of interest.