Environment Classification for Indoor/Outdoor Robotic Mapping

Abstract

We present a novel perception system for mapping of indoor/outdoor environments with an unmanned ground vehicle (UGV). The system uses image classification techniques to determine the operational environment of theUGV (indoor or outdoor). Based on the classification results, the appropriate mapping system is then deployed.Image features are extracted from video imagery andused to train a classification function using supervisedlearning techniques. This classification function is thenused to classify new imagery. A perception module observesthe classification results and switches the UGV's perception system, according to current needs and available (reliable) data as the UGV transitions from indoors to outdoors or vice versa. A terrain map that exploits GPS and Inertial Measurement Unit (IMU) data is used when operatingoutdoors, while a 2D laser based Simultaneous Localization and Mapping (SLAM) technique is used when operating indoors. Globally consistent maps are generated bytransforming the indoor map data into the global referenceframe, a capability unique to this algorithm.