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The ability to both recognize and discover terrain characteristics is animportant function required for many autonomous ground robots such as socialrobots, assistive robots, autonomous vehicles, and ground exploration robots.Recognizing and discovering terrain characteristics is challenging becausesimilar terrains may have very different appearances (e.g., carpet comes inmany colors), while terrains with very similar appearance may have verydifferent physical properties (e.g. mulch versus dirt). In order to address theinherent ambiguity in vision-based terrain recognition and discovery, wepropose a multi-modal self-supervised learning technique that switches betweenaudio features extracted from a mic attached to the underside of a mobileplatform and image features extracted by a camera on the platform to clusterterrain types. The terrain cluster labels are then used to train an image-basedconvolutional neural network to predict changes in terrain types. Throughexperiments, we demonstrate that the proposed self-supervised terrain typediscovery method achieves over 80% accuracy, which greatly outperforms severalbaselines and suggests strong potential for assistive applications.

Audio-Visual Self-Supervised Terrain Type Discovery for Mobile Platforms