Many mobile robots rely on 2D laser scanners for localization, mapping, and
navigation. However, those sensors are unable to correctly provide distance to
obstacles such as glass panels and tables whose actual occupancy is invisible
at the height the sensor is measuring. In this work, instead of estimating the
distance to obstacles from richer sensor readings such as 3D lasers or RGBD
sensors, we present a method to estimate the distance directly from raw 2D
laser data. To learn a mapping from raw 2D laser distances to obstacle
distances we frame the problem as a learning task and train a neural network
formed as an autoencoder. A novel configuration of network hyperparameters is
proposed for the task at hand and is quantitatively validated on a test set.
Finally, we qualitatively demonstrate in real time on a Care-O-bot 4 that the
trained network can successfully infer obstacle distances from partial 2D laser
readings.Comment: In 2018 IEEE/RSJ International Conference on Intelligent Robots and
Systems (IROS