This study addresses pulley position errors that inevitably arise during the installation of large wall-facing planar cable-driven parallel robots (CDPR). We propose a low-cost calibration method that integrates depth, visual, and inertial sensing using an RGB-D camera mounted on the end-effector. First, we construct kinematic and static models that incorporate pulleys, as well as fin-like chains, which are a distinctive characteristic of our CDPR. Next, we develop a motion estimation method by combining depth cameras and IMUs. Based on the estimated trajectories, we then formulate an optimization framework to minimize the discrepancy between the measured cable lengths and the predicted values from the model, enabling accurate pulley position estimation. Finally, experimental results demonstrate that the proposed method effectively estimates the motion trajectories of the moving platform and significantly reduces pulley position errors, validating its feasibility and practicality.