激光同步定位与地图构建（simultaneous localization and mapping,SLAM）数字仿真为移动测量系统的研发提供了高效、可控且可复现的虚拟测试环境，可大幅减少对复杂外业测试的依赖。然而，现有方法多依赖三维建模，面临建模成本高、几何失真及难以同步生成多传感器数据等问题。为此，本文提出了一种基于真实点云直接驱动的激光SLAM的移动测量数字仿真系统构建方法。直接将原始激光点云数据转化为具有真实场景特征的虚拟环境，通过对激光雷达与惯性测量单元核心传感器进行逆向数字建模，并采用稀疏体素网格索引与光线投射算法，来模拟传感器在采集轨迹上的行为与状态变化。系统支持B样条曲线轨迹自主规划及扫描分辨率、视场角等参数的灵活配置，能够同步生成具有时空一致性的激光雷达原始数据与惯性测量单元数据。实验结果表明：相较于传统建模的仿真方法，本文方法显著降低了场景搭建的成本与技术门槛，同时，在相同场景与采集轨迹条件下，点云几何误差即均方根误差和平均绝对误差较对比方法分别下降约18%～33%、40%～48%，表明该方法能有效解决传统建模的高门槛问题，为SLAM算法评估及设备研发提供高几何保真度的虚拟测试手段。
Laser SLAM digital simulation provides an efficient, controllable, and reproducible virtual test environment for the development of mobile measurement systems, significantly reducing the reliance on complex field tests. However, existing methods mostly rely on three-dimensional modeling, which face problems such as high modeling costs, geometric distortion, and difficulty in synchronously generating multi-sensor data. Therefore,a method for constructing a digital simulation system for mobile measurement based on laser simultaneous localization and mapping(SLAM) directly driven by real point clouds was proposed in this paper. The original laser point cloud data were directly transformed into a virtual environment with real scene characteristics. The behaviors and state changes of the sensors on the acquisition trajectory were simulated through the inverse digital modeling of the core sensors of laser radar and inertial measurement unit and the adoption of sparse voxel grid index and ray casting algorithm. The system supported the independent planning of B-spline curve trajectories and the flexible configuration of parameters such as scanning resolution and field of view and could synchronously generate spatiotemporally consistent raw laser radar data and inertial measurement unit data. Experimental results show that compared with traditional modeling-based simulation methods, the proposed method significantly reduces the cost and technical threshold of scene construction. Meanwhile, under the same scene and acquisition trajectory conditions, the geometric errors of point clouds, namely root mean square error(RMSE) and mean absolute error(MAE),decrease by approximately 18%～33% and 40%～48%, respectively, compared with the comparison methods. This indicates that the proposed method can effectively solve the high threshold problem of traditional modeling and provides a virtual test means with high geometric fidelity for SLAM algorithm evaluation and equipment development.
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