Three‐dimensional displacement monitoring over long distances has been a long‐standing concern in the structural health monitoring industry. In this study, a multi‐degree‐of‐freedom slope displacement monitoring method is developed by fusing computer vision and the 3D point triangulation method. Attributed to this method, the problems of outdoor binocular camera calibration, multi‐target mismatching, and outdoor illumination effects were solved. First, a two‐stage camera calibration method is proposed to accurately calibrate intrinsic and extrinsic camera parameters under a large field of view and long working distance conditions. Second, the adaptive spatial‐frequency method is proposed to calculate the coding and pixel coordinates of the monitored target. In this step, to solve the problem of mismatching monitored points in different camera frames, the Augmented Reality University of Cordoba code is introduced to provide a unique identity code for each monitored point. To mitigate the impact of illumination and other factors on pixel coordinate calculation, an adaptive pixel coordinate calculation method that combines information from the spatial and frequency domains is proposed., Third, based on the intrinsic and extrinsic parameters of the stereo camera and the pixel coordinates of the monitored points, the 3D coordinates of the monitored points are obtained through triangulation. Finally, the accuracy experiments and stability experiments are conducted. According to the results of the experiments, the measurement distance is positively correlated with the measurement error. And the baseline length is negatively correlated with the measurement error in the z‐direction. Ultimately, we suggest that the ratio of baseline length to measurement distance should be greater than 40%. When the recommended value is satisfied, the measurement error is less than 1 mm when the measurement distance is less than 40 m. When the measurement distance is equal to 90 m, the measurement error is less than 5 mm. Meanwhile, stability experiments of the algorithm were carried out, and in a period of outdoor validation experiments, the fluctuations were only sub‐millimeter, demonstrating good anti‐interference performance. Moreover, the method proposed in this study successfully monitored a landslide disaster in Guangxi, which demonstrated its outstanding practical application capabilities.

中文翻译：

---

基于立体视觉的边坡位移多自由度监测方法

长距离三维位移监测一直是结构健康监测行业长期关注的问题。本研究通过融合计算机视觉和三维点三角测量方法，开发了一种多自由度边坡位移监测方法。该方法解决了室外双目相机标定、多目标失配、室外光照效果等问题。首先，提出了一种两级相机标定方法，可以在大视场和长工作距离条件下精确标定相机的内在和外在参数。其次，提出了自适应空间频率方法来计算监控目标的编码和像素坐标。在这一步中，为了解决不同摄像机帧中监控点不匹配的问题，引入了科尔多瓦增强现实大学代码，为每个监控点提供唯一的身份代码。为了减轻光照等因素对像素坐标计算的影响，提出了一种结合空间域和频域信息的自适应像素坐标计算方法。第三，基于立体相机的内外参数和像素坐标。监测点的三维坐标通过三角测量获得。最后进行了精度实验和稳定性实验。根据实验结果，测量距离与测量误差呈正相关。并且基线长度与测量误差呈负相关。z-方向。最终，我们建议基线长度与测量距离的比例应大于40%。在满足推荐值的情况下，测量距离小于40 m时，测量误差小于1 mm。当测量距离等于90 m时，测量误差小于5 mm。同时，对该算法进行了稳定性实验，在一段室外验证实验中，波动仅为亚毫米级，表现出良好的抗干扰性能。此外，本研究提出的方法成功监测了广西的一次滑坡灾害，展示了其突出的实际应用能力。