Autonomous mobile robots have been widely used in national defense, disaster relief and other fields. As a typical environmental target, the stair area needs to be accurately recognized by the robot. Obstacles placed in the stair area will destroy the stairs’ plane and edge features that traditional staircase recognition algorithms need to extract, resulting in the staircase area cannot be accurately recognized. Aiming at this problem, a point cloud-based stair area detection and recognition algorithm in a complex environment is proposed. The algorithm first uses the region growing method to segment the target region and selects the regions suspected to be the vertical step of each level of the stair by fitting the plane normal direction of each region, then processes the each level stair area to segment obstacles and obtain the boundaries on the vertical plane of each level of the stair. Finally, the stair model and obstacle position are obtained according to the boundary position of each level. The experimental results show that the algorithm has better robustness, can recognize stairs in various complex environments and get barrier-free stair area. The constructed 3D model of stairs has a size error of less than 7%, which is higher accuracy. The algorithm can achieve better detection and recognition results compared with traditional stairs recognition algorithms.