Abstract
Reliable outdoor deployment of mobile robots requires the robust identification of permissible driving routes in a given environment. The performance of LiDAR and vision-based perception systems deteriorates significantly if certain environmental factors are present e.g. rain, fog, darkness. Perception systems based on Frequency-Modulated Continuous Wave scanning radar maintain full performance regardless of environmental conditions and with a longer range than alternative sensors. Learning to segment a radar scan based on driveability in a fully supervised manner is not feasible as labelling each radar scan on a bin-by-bin basis is both difficult and time-consuming to do by hand. We therefore weakly supervise the training of the radar-based classifier through an audio-based classifier that is able to predict the terrain type underneath the robot. By combining odometry, GPS and the terrain labels from the audio classifier, we are able to construct a terrain labelled trajectory of the robot in the environment which is then used to label the radar scans. Using a curriculum learning procedure, we then train a radar segmentation network to generalise beyond the initial labelling and to detect all permissible driving routes in the environment.
Useful links
Paper: IEEE
Bibtex
@INPROCEEDINGS{9294415,
author={Williams, David and De Martini, Daniele and Gadd, Matthew and Marchegiani, Letizia and Newman, Paul},
booktitle={2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)},
title={Keep off the Grass: Permissible Driving Routes from Radar with Weak Audio Supervision},
year={2020},
volume={},
number={},
pages={1-6},
keywords={Radar;Training;Robots;Spectrogram;Labeling;Trajectory;Laser radar;radar;audio;terrain classification;weakly supervised learning},
doi={10.1109/ITSC45102.2020.9294415}}