Reliable pipeline inspection is critical to safe energy transportation, but is constrained by long distances, complex terrain, and risks to human inspectors. Unmanned aerial vehicles provide a flexible sensing platform, yet reliable autonomous inspection remains challenging. This paper presents an autonomous quadrotor near-proximity pipeline inspection framework for three-dimensional scenarios based on image-based visual servoing model predictive control (VMPC). A unified predictive model couples quadrotor dynamics with image feature kinematics, enabling direct image-space prediction within the control loop. To address low-rate visual updates, measurement noise, and environmental uncertainties, an extended-state Kalman filtering scheme with image feature prediction (ESKF-PRE) is developed, and the estimated lumped disturbances are incorporated into the VMPC prediction model, yielding the ESKF-PRE-VMPC framework. A terrain-adaptive velocity design is introduced to maintain the desired cruising speed while generating vertical velocity references over unknown terrain slopes without prior terrain information. The framework is validated in high-fidelity Gazebo simulations and real-world experiments. In real-world tests, the proposed method reduces RMSE by 52.63% and 75.04% in pipeline orientation and lateral deviation in the image, respectively, for straight-pipeline inspection without wind, and successfully completes both wind-disturbance and bend-pipeline tasks where baseline method fails. An open-source nano quadrotor is modified for indoor experimentation.

翻译：可靠的管道检测对能源安全运输至关重要，但受限于长距离、复杂地形及巡检人员安全风险。无人机提供了灵活的传感平台，然而实现可靠的自主检测仍具挑战。本文提出一种基于图像视觉伺服模型预测控制（VMPC）的自主四旋翼近距三维管道检测框架。统一预测模型将四旋翼动力学与图像特征运动学耦合，实现控制回路中的直接图像空间预测。针对低帧率视觉更新、测量噪声及环境不确定性，开发了带图像特征预测的扩展状态卡尔曼滤波方案（ESKF-PRE），并将估计的集总扰动纳入VMPC预测模型，形成ESKF-PRE-VMPC框架。引入地形自适应速度设计，可在无先验地形信息条件下保持期望巡航速度，同时生成未知地形坡度上的垂直速度参考。该框架通过高保真Gazebo仿真及真实世界实验验证。在无风直管检测真实实验中，所提方法将管道方向与图像横向偏差的均方根误差分别降低52.63%和75.04%，并在基准方法失效的风扰及弯管任务中成功完成检测。实验采用经改装的纳米四旋翼开源平台进行室内验证。