Monocular Vision-Based Obstacle Height Estimation for Mobile Robot

For a robot to operate robustly in diverse real-world environments, reliable obstacle perception is essential, which fundamentally requires depth information of the surrounding scene. Monocular depth estimation provides a lightweight alternative to active sensors by predicting depth from a single RGB image. However, due to the absence of sufficient geometric and optical cues, it suffers from inherent depth ambiguity. To address this limitation, we propose R-Depth Net, a monocular absolute depth estimation network that utilizes distance-dependent defocus blur variations and optical flow as complementary depth signals. Furthermore, based on the depth maps generated by R-Depth Net, we design an algorithm for obstacle height estimation and traversability assessment. Experimental results in real-world environments show that the proposed method achieves an average RMSE of 0.30 m (15.7%) and MAE of 0.26 m (15.7%) for distance estimation within the 1.0–3.0 m range. For obstacle height estimation in the range of 0.10–0.20 m, the system achieves an average RMSE of 0.048 m (29.3%) and MAE of 0.040 m (26.4%). Finally, real-time deployment on a quadruped robot demonstrates that the estimated depth and height are sufficiently accurate to support on-board obstacle traversal decision-making.