DeepAI AI Chat
Log In Sign Up

Domain Adaptation Using Adversarial Learning for Autonomous Navigation

by   Jaeyoon Yoo, et al.

Autonomous navigation has become an increasingly popular machine learning application. Recent advances in deep learning have also brought huge improvements to autonomous navigation. However, prior outdoor autonomous navigation methods depended on various expensive sensors or expensive and sometimes erroneously labeled real data. In this paper, we propose an autonomous navigation method that does not require expensive labeled real images and uses only a relatively inexpensive monocular camera. Our proposed method is based on (1) domain adaptation with an adversarial learning framework and (2) exploiting synthetic data from a simulator. To the best of the authors' knowledge, this is the first work to apply domain adaptation with adversarial networks to autonomous navigation. We present empirical results on navigation in outdoor courses using an unmanned aerial vehicle. The performance of our method is comparable to that of a supervised model with labeled real data, although our method does not require any label information for the real data. Our proposal includes a theoretical analysis that supports the applicability of our approach.


page 2

page 6

page 13


Autonomous UAV Navigation with Domain Adaptation

Unmanned Aerial Vehicle(UAV) autonomous driving gets popular attention i...

Adversarial Learning and Self-Teaching Techniques for Domain Adaptation in Semantic Segmentation

Deep learning techniques have been widely used in autonomous driving sys...

Ensemble of Discriminators for Domain Adaptation in Multiple Sound Source 2D Localization

This paper introduces an ensemble of discriminators that improves the ac...

Synthetic Data for Deep Learning

Synthetic data is an increasingly popular tool for training deep learnin...

Deep Domain Adaptation for Detecting Bomb Craters in Aerial Images

The aftermath of air raids can still be seen for decades after the devas...

Domain Confusion with Self Ensembling for Unsupervised Adaptation

Data collection and annotation are time-consuming in machine learning, e...

Autonomous Navigation in Confined Waters – A COLREGs Rule 9 Compliant Framework

Fully or partial autonomous marine vessels are actively being developed ...