Abstract: Indoor space is difficult to build 3D maps with high accuracy using limited sensors due to low illumination, lack of GPS positioning assistance and few scene features. To address this problem, this paper improves the FAST-LIO algorithm by introducing a Lider-IMU parameter initialization system and a back-end loopback detection optimization algorithm to increase the robustness of map building in large scenes. Experimental studies are conducted using publicly available datasets. The results show that the trajectory error accuracy of the algorithms in this paper are improved compared with the existing algorithms. This paper also designs a robot to test in the interior environment of a building at Southeast University. The experimental results show that the robot can realize the autonomous movement to build the map and return safely with good results.

Key words: mobile robots, autonomous exploration, 3D map building, point cloud maps

摘要： 室内空间由于低照明、缺少GPS定位辅助和场景特征较少等原因难以利用有限传感器进行高精度三维建图。针对此问题，对FAST-LIO算法进行改进，引入了Lider-IMU参数初始化系统和后端回环检测优化算法，以增加大场景下的建图鲁棒性。采用公开数据集进行实验研究。结果表明，和现有算法相比，该算法轨迹误差精度均有提升。还设计了机器人在东南大学的建筑内部环境进行测试。实验结果表明，机器人能够实现自主移动建图并安全返回，效果良好。

关键词: 移动机器人, 自主探索, 三维建图, 点云地图