Research on Staircase Feature Recognition and 3D Point Cloud Modeling for Robot Obstacle Crossing

To enhance the autonomous obstacle crossing ability of mobile robots in complex environments, this paper focuses on the staircase scene and proposes a staircase feature recognition and 3D point cloud modeling method for robot obstacle crossing tasks. This method first utilizes a binocular vision system to obtain dense point cloud data, and effectively filters out noise and separates the ground through point cloud preprocessing algorithms. Subsequently, a staircase detection algorithm was designed that integrates geometric constraints and depth features to achieve precise extraction of staircase step edges, treads, and elevations. On this basis, this article constructed a three-dimensional staircase model and accurately extracted key structural parameters such as step height, depth, slope, and staircase orientation, providing a reliable basis for robot obstacle path planning and attitude adjustment. To address the challenge of parameter selection in complex environments, this paper further introduces an adaptive parameter adjustment mechanism based on reinforcement learning. Comparative experiments in practical scenarios have shown that our proposed method significantly outperforms traditional methods in terms of stair recognition accuracy, modeling precision, and computational efficiency, demonstrating good robustness and practical value. This study provides a solid theoretical foundation and technical support for the autonomous perception and intelligent obstacle crossing of mobile robots in typical obstacle environments such as stairs.