An attempt to implement the approach proposed in "Learning Semantic Place Labels from Occupancy Grids using CNNs" by Goeddel and Olson
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.
Goeddel, Robert, and Edwin Olson. "Learning semantic place labels from occupancy grids using CNNs." 2016 IEEE/RSJ international conference on intelligent robots and systems (IROS). IEEE, 2016.
Dataset and Trained Model available here
The dataset consists of about 75,000 images which were generated from 2D range scans captured in different environments. The range scans were obtained from the datasets hosted by Oscar Martinez Mozos.