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Reliable outdoor deployment of mobile robots requires the robustidentification of permissible driving routes in a given environment. Theperformance of LiDAR and vision-based perception systems deterioratessignificantly if certain environmental factors are present e.g. rain, fog,darkness. Perception systems based on FMCW scanning radar maintain fullperformance regardless of environmental conditions and with a longer range thanalternative sensors. Learning to segment a radar scan based on driveability ina fully supervised manner is not feasible as labelling each radar scan on abin-by-bin basis is both difficult and time-consuming to do by hand. Wetherefore weakly supervise the training of the radar-based classifier throughan audio-based classifier that is able to predict the terrain type underneaththe robot. By combining odometry, GPS and the terrain labels from the audioclassifier, we are able to construct a terrain labelled trajectory of the robotin the environment which is then used to label the radar scans. Using acurriculum learning procedure, we then train a radar segmentation network togeneralise beyond the initial labelling and to detect all permissible drivingroutes in the environment.

Keep off the Grass: Permissible Driving Routes from Radar with Weak Audio Supervision