Stair-mapping with Point-cloud Data and Stair-modeling for Quadruped Robot

In this paper, we present a mapping method of stairs for quadruped robots based on point-cloud measurements and stair-modeling. Because of the quadruped robot's physical property, the distance between the robot's vision sensor and the stair is short and the detecting range of the point-cloud sensor is narrow when the robot navigates a stair environment. This causes many problems, for example, difficulties in finding features on the image or tracking them. As a result, vision-only based odometry becomes unreliable. What we propose here is to use the estimation model of stairs fused with point-cloud measurements from a depth sensor. By combining sensor measurement and estimation data from the regular shape of stairs, we overcome the disadvantage of mapping that comes from the limited measurement distance between the object and the sensor. We use a clustering algorithm for stairs based on the surface normal directions of the stair surfaces and their global coordinates and this method provides us robust and reliable clustering results. Finally, we show the performance of the implemented ideas in experiments with hand-held sensors as well as with a quadruped robot.