  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 FMCW 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.
