Stair Feature Recognition and Modeling based on Binocular Vision Autonomous obstacle crossing for mobile robots is an important area of research. The premise of autonomous obstacle crossing is the ability of a robot to sense the environment and determine the size of obstacles. This paper uses stairs as the research object, which is the most common and complex obstacle model in structured environments. By analyzing the structural features of stairs, a method to extract the geometric features of stairs from a point cloud is proposed. From the geometric dimensions of the stairs, the data related to robot obstacle crossing are extracted to construct an obstacle model. The experimental results show that this method can effectively extract the characteristics of stairs and provide an obstacle model that can meet the requirements for obstacle crossing.

Real Time Obstacle Avoidance and Navigation with Mobile Robot via Local Elevation Information This paper considers the realtime navigation and obstacle avoidance of mobile robots in complex environments. A novel local environment perception and navigation framework for mobile robot navigation based on the elevation information is proposed. A local elevation map is introduced to perceive and map obstacles in the local environment. Calculating obstaclesxe2x80x99 heights and shapes by local elevation maps, the terrain traversability of the surrounding environment of a robot is determined, and a twodimensional local occupancy grid map for robot path planning and navigation in local environment is generated. The effectiveness of our approach is validated experimentally through realtime navigation and obstacle avoidance in the artificial environment. The experimental results show that using the proposed framework, a mobile robot can safely reach its target position while avoiding the noncrossable area. For indoor mobile robots with limited computing power, in complex environments, it could provides more safe navigation and pathplanning.