Self-Supervised Sensor Learning and Its Application: Building 3D Semantic Maps Using Terrain Classification

Abstract

An autonomous robot in an outdoor environment needs to recognize the surrounding environment to move to a desired location safely; that is, a map is needed to classify/perceive the terrain. This paper proposes a method that enables a robot to classify a terrain in various outdoor environments using terrain information that it recognizes without the assistance of a user; then, it creates a three-dimensional (3D) semantic map. The proposed self-supervised learning system stores data on the appearance of the ground data using image features extracted by observing the movement of humans and vehicles while the robot is stopped. It learns about the surrounding environment using a support vector machine with the stored data, which is divided into terrains where people or vehicles have moved and other regions. This makes it possible to learn which terrain an object can travel on using a self-supervised learning and image-processing methods. Then the robot can recognize the current environment and simultaneously build a 3D map using the RGB-D iterative closest point algorithm with a RGB-D sensor (Kinect). To complete the 3D semantic map, it adds semantic terrain information to the map.