Real-Time Stairs Geometric Parameters Estimation for Lower Limb Rehabilitation Exoskeleton

Stairs are common structures that hinder the rehabilitation exoskeleton applications in the artificial environment. It is impossible for the exoskeleton to save all the geometric parameters such as height and depth of each stair in real-world. By detection and modeling the stairs with computer vision, this paper provides the possibility of wearing rehabilitation exoskeleton for training hemiplegia patients on stairs. Based on the point cloud reconstructed from RGB-D data, normal of each point are computed firstly. We subsequently apply over-segmentation then re-aggregation on the point cloud and normals to extract planes exhaustively. Finally, a stairs graph are modeled based on this planes and the geometric parameters are computed based on the stairs model. Our algorithm is designed as simple as possible to reach the real-time requirement for practical situations. We evaluated this stairs modeling algorithm on two situations. The results indicate that it achieves equivalent precision to state-of-art approaches and even works on partial occlusion conditions.