【摘要】 [目的]针对隧道检测效率低和数据精度受限的问题，本文提出了基于多传感器融合的隧道检测方法。[方法]在不同检测速度的应用场景下，通过集成断面激光扫描仪、惯导、线阵相机，构建多种隧道智能检测系统。针对隧道环境中GNSS信号缺失的问题，提出了一种结合惯导与激光点云控制点约束的轨迹优化方法，引入非线性最小二乘与迭代优化，实现毫米级轨迹解算精度。通过建立激光雷达与相机之间的时空配准模型，实现图像与点云数据的融合重建，生成具有真实颜色信息的三维点云模型。[结果]试验结果表明，在卫星拒止环境下，该系统的轨迹重建平均误差控制在5 mm以内。与传统人工巡检方式相比，检测效率提高约3倍。[结论]研究成果验证了该系统在隧道结构变形监测、竣工验收中的工程适用性与稳定性。更多还原

【Abstract】 [Purposes] To address the issues of low inspection efficiency and limited data accuracy in tunnel detection, this paper proposes a tunnel inspection method based on multi-sensor fusion.[Methods] In application scenarios with different inspection speeds, various intelligent tunnel inspection systems are developed by integrating a profile laser scanner, an inertial navigation system, and a line-scan camera.To overcome the problem of GNSS signal loss in tunnels, a trajectory optimization method combining inertial navigation data with laser point cloud control point constraints is proposed.By introducing nonlinear least squares and iterative optimization, the method achieves millimeter-level spatial accuracy in trajectory estimation.A spatiotemporal calibration model between the LiDAR and the camera is established to enable the fusion and reconstruction of image and point cloud data, resulting in a 3D point cloud model with realistic color information.[Findings] Experimental results show that in GNSS-denied environments, the trajectory reconstruction error of the system is controlled within 5 mm.Compared with traditional manual inspection methods, the detection efficiency is improved by approximately three times.[Conclusions] The research results verify the engineering applicability and stability of the system in tunnel structure deformation monitoring and completion acceptance.更多还原