GONet：半教師あり学習による走行可能領域推定
GONet: A Semi-Supervised Deep Learning Approach For Traversability Estimation
魚眼画像から走行可能領域を推定するGANベースの半教師あり手法を提案し、正例を多く負例を少数だけ使って学習できる実用的な点とリアルタイム性・汎化性を示した。
著者: Noriaki Hirose, Amir Sadeghian, Marynel Vázquez, Patrick Goebel, Silvio Savarese
分類: cs.RO, cs.CV, cs.LG
原文アブストラクト
We present semi-supervised deep learning approaches for traversability estimation from fisheye images. Our method, GONet, and the proposed extensions leverage Generative Adversarial Networks (GANs) to effectively predict whether the area seen in the input image(s) is safe for a robot to traverse. These methods are trained with many positive images of traversable places, but just a small set of negative images depicting blocked and unsafe areas. This makes the proposed methods practical. Positive examples can be collected easily by simply operating a robot through traversable spaces, while obtaining negative examples is time consuming, costly, and potentially dangerous. Through extensive experiments and several demonstrations, we show that the proposed traversability estimation approaches are robust and can generalize to unseen scenarios. Further, we demonstrate that our methods are memory efficient and fast, allowing for real-time operation on a mobile robot with single or stereo fisheye cameras. As part of our contributions, we open-source two new datasets for traversability estimation. These datasets are composed of approximately 24h of videos from more than 25 indoor environments. Our methods outperform baseline approaches for traversability estimation on these new datasets.
