Monocular Vision-Based Obstacle Height Estimation for Mobile Robot

To ensure that robots can operate reliably in diverse environments, obstacle detection is essential, which requires the acquisition of depth information of the surrounding environment. One approach for obtaining depth information is monocular depth estimation, a technique that predicts scene depth from a single camera input. However, monocular images inherently provide limited cues for depth perception, which restricts estimation accuracy. To address this limitation, we propose R-Depth Net, a monocular depth estimation network that leverages variations in blur intensity with respect to distance and frame-to-frame changes induced by motion as primary cues. Based on the depth maps generated by R-Depth Net, we developed an algorithm that enables a robot to estimate obstacle height and determine whether traversal is feasible. When applied to a quadruped robot platform, the proposed method achieved RMSE 0.30 m and MAE 0.26 m for obstacle distance estimation, and RMSE 0.048 m and MAE 0.040 m for obstacle height estimation. Furthermore, real-world obstacle traversal experiments demonstrated the effectiveness of the proposed monocular camera–based obstacle detection and traversal decision-making framework.