Learning semantic place labels from occupancy grids using CNNs

Abstract

The goal of this paper is to develop a robot with a grounded spatial vocabulary. Such a vocabulary would allow it to give and follow directions, and would give it valuable additional information in aiding localization and navigation. We approach the problem by defining an ontology of space (including corridor, doorway, and room) and by creating a Convolutional Neural Network (CNN) that allows the robot to classify LIDAR sensor data accordingly. In particular, we propose a CNN architecture that performs comparably or better than existing methods based on engineered features. Training CNNs can be fickle; we describe several specific aspects of our approach that are important for good performance in this task.