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Traversability prediction is a fundamental perception capability forautonomous navigation. The diversity of data in different domains imposessignificant gaps to the prediction performance of the perception model. In thiswork, we make efforts to reduce the gaps by proposing a novel coarse-to-fineunsupervised domain adaptation (UDA) model - CALI. Our aim is to transfer theperception model with high data efficiency, eliminate the prohibitivelyexpensive data labeling, and improve the generalization capability during theadaptation from easy-to-obtain source domains to various challenging targetdomains. We prove that a combination of a coarse alignment and a fine alignmentcan be beneficial to each other and further design a first-coarse-then-finealignment process. This proposed work bridges theoretical analyses andalgorithm designs, leading to an efficient UDA model with easy and stabletraining. We show the advantages of our proposed model over multiple baselinesin several challenging domain adaptation setups. To further validate theeffectiveness of our model, we then combine our perception model with a visualplanner to build a navigation system and show the high reliability of our modelin complex natural environments where no labeled data is available.

CALI: Coarse-to-Fine ALIgnments Based Unsupervised Domain Adaptation of Traversability Prediction for Deployable Autonomous Navigation