Pseudo-Trilateral Adversarial Training for Domain Adaptive
  Traversability Prediction
Zheng Chen Durgakant Pushp Jason M. Gregory Lantao Liu
Traversability prediction is a fundamental perception capability for
autonomous navigation. Deep neural networks (DNNs) have been widely used to
predict traversability during the last decade. The performance of DNNs is
significantly boosted by exploiting a large amount of data. However, the
diversity of data in different domains imposes significant gaps in the
prediction performance. In this work, we make efforts to reduce the gaps by
proposing a novel pseudo-trilateral adversarial model that adopts a
coarse-to-fine alignment (CALI) to perform unsupervised domain adaptation
(UDA). Our aim is to transfer the perception model with high data efficiency,
eliminate the prohibitively expensive data labeling, and improve the
generalization capability during the adaptation from easy-to-access source
domains to various challenging target domains. Existing UDA methods usually
adopt a bilateral zero-sum game structure. We prove that our CALI model -- a
pseudo-trilateral game structure is advantageous over existing bilateral game
structures. This proposed work bridges theoretical analyses and algorithm
designs, leading to an efficient UDA model with easy and stable training. We
further develop a variant of CALI -- Informed CALI (ICALI), which is inspired
by the recent success of mixup data augmentation techniques and mixes
informative regions based on the results of CALI. This mixture step provides an
explicit bridging between the two domains and exposes underperforming classes
more during training. We show the superiorities of our proposed models over
multiple baselines in several challenging domain adaptation setups. To further
validate the effectiveness of our proposed models, we then combine our
perception model with a visual planner to build a navigation system and show
the high reliability of our model in complex natural environments.